Objective To investigate the impact of intergenerational caregiving on the brain structure and function of grandparents,and to analyze its correlation with caregiving factors.Methods Healthy adults(66 with grandchildren,24 without grandchildren)were recruited as study subjects,and clinical and MRI data were collected.Resting-state brain functional degree centrality(DC)and surface-based morphometry(SBM)methods were used to compare the differences in brain structure and function between the groups with and without grandchildren.The correlation between the differences in brain regions and △ values with grandchild's age,number,and time spent in childcare were assessed,respectively.Results Compared to the group without grandchildren,the group with grandchildren showed reduced surface area and cortical volume in the left middle temporal gyrus,as well as decreased DC values in the left medial superior frontal gyrus,bilateral orbital superior frontal gyrus,and left anterior cingulate and paracingulate gyrus(P＜0.05),respectively.In the grandchildren group,DC values and △ values in the left orbital superior frontal gyrus,left anterior cingulate and paracingulate gyrus were significantly positively correlated with time spent in childcare.Conclusion The brain structures and functions of grandparents related to empathy and motivation are changed in intergenerational caregiving,which may reveal the neuroplasticity after caring for their grandchildren.

Objective To investigate the role of membrane-associated tyrosine/threonine-protein ki-nase 1(PKMYT1)in glucocorticoid(GC)-induced osteoblast(OB)apoptosis,providing a theoretical basis and potential therapeutic targets for early-stage steroid-induced avascular necrosis of the femoral head(SANFH).Methods(1)Mouse calvarial osteoblastic cells(MC3T3-E1)were selected for the study.The control group was cultured in standard medium,while the experimental group was subject-ed to osteogenic induction culture,with osteogenic capacity verified by alkaline phosphatase(ALP)and Alizarin Red S(ARS)staining.Then,mouse osteoblasts(mOB)were treated with different con-centrations of GC.After that,apoptosis was detected by using Annexin V-FITC/PI double staining as-say,while cell proliferation was assessed by using Cell Counting Kit-8(CCK8).Moreover,the expres-sions of anti-apoptotic protein B-cell lymphoma/leukemia-2(BCL-2),pro-apoptotic proteins cleaved caspase-3andcleavedcaspase-9(cleavedcaspase 3/9)weredetectedbyusing Westernblotting(WB).Meanwhile,proteomic analysis was employed to identify molecules potentially regulating GC-in-duced apoptosis in mOBs.What's more,quantitative real-time PCR(qPCR)and WB were used to further analyze PKMYT1 expression.(2)mOBs were treated with PKMYT1 inhibitor GSK-1520489A of different concentrations to screen the optimal one,and all subjects were then further divided into a control,a GC,a GSK-1520489A,and a GC+GSK-1520489A group.Later,the expression of PK-MYT1 and apoptosis-related proteins BCL-2 and cleaved caspase 3/9 of all groups were detected us-ing WB,and cell viability and cytotoxicity were evaluated by CCK8 assay,with cell proliferation by using 5-ethynyl-2'-deoxyuridine(EDU)assay and apoptosis by cell live/dead staining and Annexin V-FITC/PI double staining.(3)mOBs were infected with PKMYT1 overexpression lentiviral vectors,and its efficiency was verified by using immunofluorescence,qPCR,and WB.After successful overexpres-sion of PKMYT1,all cells were divided into the control,GC,PKMYT1 overexpression(OE),and OE+GC groups,whose cell proliferation was detected by EDU assay,and apoptosis was assessed by Annexin V-FITC/PI double staining and cell live/dead staining.(4)To verify the changes in PKMYT1 expression in human osteoblasts(hOB),hOBs extracted from human femoral heads of healthy individu-als were chosen into the control group,while those from patients with hormone-induced avascular ne-crosis of the femoral head(hSANFH)were selected into the hSANFH group.Then,PKMYT1 expres-sion in both groups was detected by using qPCR and WB.Results(1)After inducing the differentia-tion of mouse calvarial osteoblastic cells(MC3T3-E1)into mature osteoblasts,under the action of GC,compared with the control group,with the increase of GC concentration,the experimental group showed increased mOB apoptosis(P<0.01)and expression of cleaved caspase 3/9(P<0.01),but de-creased cell viability(P<0.01)and expressionof apoptosis-relatedprotein BCL-2(P<0.01).More-over,according to the proteomic sequencing,significant decrease was observed in the PKMYT1 expres-sion in mature mOBs treated with GC.(2)As to treatment of mOBs with different concentrations of PKMYT1 inhibitor GSK-1520489A,with the increase of concentration,cell viability decreased and cy-totoxicity increased(P<0.001).Moreover,compared with the control group,mOBs proliferation de-creased(P<0.001)and apoptosis increased(P<0.001)in the GSK-1520489A group.Meanwhile,com-pared with the GC group,mOB proliferation decreased(P<0.05)and apoptosis increased significantly(P<0.01)in the GC+GSK-1520489A group.(3)After overexpression of PKMYT1,in comparison with the control group,mOB proliferation increased(P<0.001)but apoptosis did not increase significantly(P>0.05)in the OE group.Moreover,compared with the GC group,mOB proliferation increased(P<0.001)but apoptosis decreased(P<0.001)significantly in the OE+GC group.(4)In hOBs extracted from human femoral head tissues,qPCR and WB results showed that PKMYT1 expression of the hSANFH group was significantly lower than the control group(P<0.001).Conclusion Down regulation of PKMYT1 expression promotes GC-induced apoptosis of mOBs.Conversely,over expression of PK-MYT1 inhibits GC-induced apoptosis of mOBs.Therefore,PKMYT1 may serve as a potential target for the early treatment of SANFH.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

OBJECTIVE To systematically investigate the changes of volatile components in Euphorbia wallichii after milk and wine processing， and preliminarily elucidate the material basis for reducing toxicity. METHODS Using headspace gas chromatography-mass spectrometry technology， the volatile components in raw E. wallichii， milk-processed E. wallichii， and wine- processed E. wallichii were isolated and identified， and the relative percentage content of each component was calculated by the peak area normalization method. Combining chemometric methods such as principal component analysis and orthogonal partial least- squares discriminant analysis， changes in volatile components in samples after milk and wine processing were compared. Differential components were screened. RESULTS A total of 66 volatile components were identified from the three samples， with the types of compounds primarily comprising alkanes， olefins， heterocycles and esters， among others. A total of 39， 24 and 36 volatile components were identified from raw E. wallichii， milk-processed E. wallichii， and wine-processed E. wallichii， respectively， with 10 components common to all three preparations. Compared with raw E. wallichii， the relative percentage of other components in milk-processed E. wallichii decreased， except for alkanes and esters. The relative percentage of alkanes， olefins， aldehydes and esters in wine-processed E. wallichii increased， but the contents of heterocyclic compounds， ketones， ethers and alcohols decreased. The results of chemometric analysis showed that the volatile components of raw and processed products were significantly different. A total of 5 kinds of differential components in milk-processed products and 3 kinds of differential components in wine-processed products were screened out. Among them， the relative percentage of potential toxic components such as linalool， octanal and 3-pentanone decreased significantly after processing（P＜0.05）. CONCLUSIONS Milk and wine processing may exert a toxicity-reducing effect by reducing the contents of toxic components such as linalool， octanal and 3-pentanonein E. wallichii.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To investigate the interactive effects of loss of the only child and childhood trauma on brain structure, function, and structure-function coupling, and to analyze their association with clinical symptom.Methods:A total of 112 parents who lost their only child and participated in the psychological aid project organized by Local Civil Affairs Department in Sunan aear of Jiangsu Province in China from April 2021 to July 2021 and 36 healthy controls recruited from the community during the same period were selected. Based on childhood trauma questionnaire scores, parents who had lost their only child were divided into those with childhood trauma (group A, n=55) and those without childhood trauma (group B, n=57); similarly, the healthy controls were divided into a group with childhood trauma (group C, n=12) and a group without childhood trauma (group D, n=24). All participants were evaluated by clinical scales such as Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Mini-Mental State Examination (MMSE). MRI 3D-T1 structural images and resting-state functional magnetic resonance imaging data were collected; gray matter volume (GMV) and degree centrality (DC) were calculated by standardized image preprocessing procedure, and ratio of DC to GMV within each voxel was computed to obtain the structure-function coupling map. A two-factor analysis of variance was used to analyze the independent effect and interactive effect of loss of the only child and childhood trauma on GMV, DC, and DC/GMV coupling value. Spearman rank correlation analysis was used to evaluate the associations of above indicators in brain regions with significant difference in independent effect and interactive effect with clinical scale scores. Results:(1) Compared with the participants without childhood trauma (group B+group D), the participants with childhood trauma (group A+group C) showed significantly reduced GMV in the left middle temporal gyrus and right dorsolateral superior frontal gyrus (voxel-level P<0.01, cluster-level P<0.05, Gaussian random field [GRF] corrected). A significant interactive effect of loss of the only child and childhood trauma on GMV in the right precuneus was observed (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). (2) Compared with the healthy controls, parents who had lost their only child exhibited significantly increased DC in the left middle frontal gyrus (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). Compared with participants without childhood trauma, participants with childhood trauma showed significantly increased DC in the right thalamus (voxel-level P<0.01, cluster-level P< 0.05, GRF corrected). A significant interactive effect of loss of the only child and childhood trauma on DC in the left dorsolateral superior frontal gyrus was observed (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). (3) Compared with the healthy controls, parents who had lost their only child showed significantly decreased DC/GMV coupling value in the left middle frontal gyrus (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). Compared with participants without childhood trauma, participants with childhood trauma showed significantly increased DC/GMV coupling value in the right thalamus (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). A significant interactive effect of loss of the only child and childhood trauma on DC/GMV coupling value in the left dorsolateral superior frontal gyrus was observed (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). (4) Correlation analysis revealed that GMV in the right precuneus with significant interactive effect of loss of the only child and childhood trauma was positively correlated with MMSE score ( r s=0.317, P=0.010, Bonferroni corrected). GMV in the left middle temporal gyrus with significant independent effect of childhood trauma was positively correlated with both HAMD score and HAMA score ( r s=0.362, P=0.006; r s= 0.349, P=0.008, Bonferroni corrected). Conclusion:Loss of the only child and childhood trauma can interact to jointly affect the brain structure, function, and structure-function coupling; and some of these brain structure alterations are closely associated with clinical symptoms.

Objective:To explore the influencing factors of olfactory impairment in patients with obstructive sleep apnea（OSA） and establish a nomogram prediction model. Methods:A total of 100 OSA patients were enrolled. Snap&Sniff olfactory test was used to evaluate the olfactory identification function and olfactory threshold of the patients. According to the scoring criteria, either olfactory identification scores below 14 points or olfactory threshold scores below 3 points was defined as olfactory impairment. Multivariate logistic regression analysis was used to explore the influencing factors of olfactory impairment in OSA. The nomogram model was constructed by using the R 4.4.2 software package. ROC curve, calibration curve and decision curve were used to evaluate the predictive efficacy, consistency and clinical utility of the model. Results:A total of 55 of 100 OSA patients had olfactory impairment. The results of multivariate logistic regression analysis showed that age, ESS score, MoCA score, and apnea-hypopnea index（AHI） were the influencing factors of olfactory impairment in OSA. Based on the above parameters, a nomogram model was established. The ROC curve analysis showed that the AUC was 0.897（95%CI 0.834-0.961）, indicating that the model had good predictive ability. The calibration curve showed that the predicted probability of the model fits the actual probability well. Decision curve analysis showed that when the threshold probability was in the range of 0-0.9, the model had a high clinical net benefit rate. Conclusion:Age, ESS score, MoCA score and AHI are the influencing factors of olfactory impairment in patients with OSA. The nomogram model constructed based on the above factors has good predictive value, which is conducive to the clinical multi-angle understanding of OSA and the formulation of scientific prevention and treatment measures.
Humans ; Sleep Apnea, Obstructive/physiopathology* ; Nomograms ; Olfaction Disorders/etiology* ; Logistic Models ; Middle Aged ; Male ; Female ; ROC Curve ; Adult ; Aged

Objective:To explore the RNA expression and alterations in brain structure in individuals who have experienced stressful life events (SLE), as well as the correlation patterns between them and their association with the occurrence of depression.Methods:Prospectively, a total of 80 SLE subjects were recruited from the psychiatry and psychology clinic of the Jiangsu University Affiliated Yixing Hospital between January 2021 and December 2022, with 16 normal controls (NC) enrolled concurrently. The 17 items Hamilton depression scale (HAMD-17) and social readjustment rating scale (SRRS) were used to assess depressive symptoms and stress levels. RNA sequencing information of peripheral blood and imaging data at baseline were collected. Based on whether depression occurred during the 2-year follow-up period, SLE subjects were divided into the SLE-depression group ( n=15) and the SLE-non-depression group ( n=65). Differentially expressed genes (DEGs) were screened using differential analysis and protein-protein interaction (PPI) networks. Fractional anisotropy (FA) of white matter tracts and gray matter volume (GMV) were extracted using tract-based spatial statistics and voxel-based morphometry.Using analysis of variance compared inter-group differences in gene expression, GMV and white matter FA values. Partial correlation analysis was used to explore correlations between DEGs, altered GMV and white matter microstructure. Gene set enrichment analysis (GSEA) was performed on key genes to identify potential biological pathways. Propensity score matching constructed sensitivity subgroups to verify result robustness. Results:The SLE-depression group showed significantly higher SRRS and HAMD-17 scores at baseline and at the end of follow-up compared to the SLE-non-depression group and the NC group ( H=47.773, 35.427, 41.114, all P<0.05). Expression levels of IL-10 (2.12±0.28, 2.43±0.44), EZH2 (2.11±0.43, 2.45±0.51), NCAM1 (3.60±0.30, 3.03±0.39), CD3E (4.95±0.37, 4.57±0.48), CCK (3.29±0.28, 3.02±0.42), and CX3CR1 (5.55±0.40, 5.91±0.34) were significantly different between the SLE-depression group and SLE-non-depression group( F=5.549~28.371, all P<0.05). Compared with the SLE-non-depression group, the SLE-depression group exhibited significantly lower FA values in the genu of the corpus callosum (0.29±0.04, 0.31±0.04) and the left uncinate fasciculus (0.31±0.02, 0.33±0.02), as well as significantly smaller GMV in the right hippocampus (0.29±0.07, 0.33±0.06), bilateral middle frontal gyrus (left: 0.27±0.05, 0.31±0.05; right: 0.28±0.06, 0.32±0.06), right insula (0.36±0.03, 0.38±0.04), and left precentral gyrus (0.19±0.04, 0.24±0.05) ( F=4.593-12.064, all P<0.05, FDR correction). GMV in the right anterior cingulate and paracingulate gyri was significantly larger than that in the SLE-non-depression group (0.34±0.05, 0.29±0.06) ( F=6.704, P=0.034, FDR correction). Partial correlation analysis revealed significantly stronger correlations between hub DEGs and altered brain regions in the SLE-depression group ( r=0.017-0.801) compared to the SLE-non-depression group ( r=0.002-0.382), with a statistically significant difference ( U=629, P<0.001; Cliff's Delta=0.454). GSEA indicated that the aforementioned genes were primarily involved in pathways including the ribosome, spliceosome, ribosome biogenesis in eukaryotes, and neuroactive ligand-receptor interaction. Sensitivity analysis confirmed that the above results remained statistically significant after balancing sample sizes (all P<0.05). Conclusion:The SLE-depression group showed specific RNA expression and brain structure alterations compared to the SLE-non-depression group, and the correlation between RNA and brain structure was significantly enhanced in the SLE-depression group. This suggests that the correlation between genes and brain structure in the SLE population may be related to their susceptibility to depression.

Objective:To investigate the predictive value of multimodal magnetic resonance imaging (MRI) techniques in assessing symptom remission of post-traumatic stress disorder (PTSD) of bereaved parents who lost their only child.Methods:In this prospective study, 34 parents with PTSD resulting from the loss of the only child were followed-up for 2 years. Based on the PTSD diagnostic status at the end of the follow-up, participants were divided into the remission group and the persistent group.R 3.6.1 and SPSS 20.0 software were used for statistical analysis.Baseline clinical data and neuroimaging findings were compared between the two groups. Logistic regression and LASSO regression analyses were used to identify independent predictors of PTSD symptom remission. The predictive performance of these factors was evaluated by receiver operating characteristic (ROC) curve analysis.Results:Initial screening with univariate Logistic regression and LASSO regression revealed that regional homogeneity (ReHo) in the left middle temporal gyrus, the combined predictive value based on ReHo, and the integrated predictive value combining gray matter volume (GMV) and ReHo (GMV-ReHo predictor) were significant factors influencing symptom remission (all P<0.05). Multivariate Logistic regression further demonstrated that the GMV-ReHo predictor retained independent predictive significance ( P<0.05), with ROC curve analysis showing an area under the curve (AUC) of 0.979 (95% CI=0.935-0.996, P<0.001) for its ability to predict PTSD remission. Notably, a combined model incorporating both the scores of the clinician administered PTSD scale (CAPS) and the GMV-ReHo predictor achieved an enhanced predictive performance, yielding an AUC of 0.984 (95% CI=0.952-0.998, P<0.001). Conclusion:The GMV-ReHo predictor effectively identifies symptom remission in PTSD resulting from the loss of the only child.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network between participants with subjective cognitive decline (SCD) and healthy individuals, and to analyze the correlations of the volumes of the different subfields and altered covariance brain regions with memory function.Methods:A total of 57 SCD individuals(SCD group) and 44 normal controls(NC group) participants were assessed for memory function using composite scores from the auditory verbal learning test (AVLT) and the Wechsler memory scale visual reproduction (VR) test from June 2022 to October 2023.T1-weighted structural magnetic resonance imaging (MRI) data were collected from all participants, and hippocampal subfields, cortical regions, and subcortical nuclei were segmented using FreeSurfer to measure the gray matter volume of each structure. A structural covariance network was constructed based on the correlation of gray matter volumes across regions. Statistical analysis was performed using R 4.3.1 software. Inter-group differences in hippocampal subfield volumes were compared using multivariate analysis of covariance. Differences in structural covariance connectivity between groups were assessed using Z-test, while network topology differences were compared through permutation testing. Finally, partial correlation analysis was used to examine correlation of the volumes of the differential hippocampal subfields and covariance brain regions with memory function. Results:The SCD group exhibited significantly lower years of education, AVLT-immediate score, AVLT-delayed score, VR-immediate score, VR-delayed score, and memory function Z-score compared to the NC group ( t=2.064, 3.888, 2.622, 3.222, 4.761, 5.184, all P<0.05). The volumes of the right subiculum((387.75±55.20)mm 3, (352.70±70.25)mm 3), left presubiculum((263.12±38.52)mm 3, (239.79±46.02)mm 3), left subiculum((388.12±49.34)mm 3, (351.74±67.30)mm 3) and left CA1((571.01±80.01)mm 3, (526.51±98.80)mm 3) in the SCD group were smaller than the corresponding volumes in NC group ( F=9.139, 8.039, 11.207, 7.266, all P<0.05, FDR correction). Differences in structural covariance connectivity were found between the SCD and NC groups in the following pairs: right CA1-right subiculum, right CA1-left subiculum, right CA3-left parasubiculum and right hippocampus-amygdala transition area-left subiculum ( Z=-3.848, -3.896, -3.597, -3.895, all P<0.05, FDR correction).Partial correlation analysis revealed that in the SCD group, the volume of the left subiculum ( r=0.359, P=0.007), left CA1 ( r=0.430, P=0.001), right entorhinal cortex ( r=0.296, P=0.029), right middle temporal gyrus ( r=0.361, P=0.007), right parahippocampal gyrus ( r=0.313, P=0.021)were positively correlated with the total memory function score. Conclusion:Hippocampal subfields atrophy, as well as alterations in structural covariance network, have been found in SCD individuals. Furthermore, the decline in memory function may be closely associated with atrophy in hippocampal subfields and structurally covariant regions.