Objective:To investigate the changes of abnormal spontaneous brain activity and whole-brain effector connectivity in patients with obsessive-compulsive disorder (OCD) by combining low frequency amplitude (ALFF) and Granger causality analysis (GCA), and explore their relations with clinical symptoms.Methods:Forty-nine patients with OCD admitted to Department of Psychiatry, Second Affiliated Hospital of Xinxiang Medical College from January 2020 to September 2023 were selected as OCD group; 50 healthy volunteers matched with gender, age and years of education were enrolled as healthy control (HC) group. Obsessive-compulsive symptoms and severities in the OCD group were assessed by Yale Brown obsessive-compulsive scale (Y-BOCS). All subjects underwent whole-brain resting-state functional magnetic resonance imaging scanning (rs-fMRI). ALFF differences between the 2 groups were compared. Brain regions with ALFF differences were used as seed points, and effector connectivity changes in seed points were compared with those in whole-brain by GCA. Correlations of ALFF and effector connectivity in brain regions with ALFF differences with total scores, obsession scores and compulsion scores of Y-BOCS were analyzed by partial correlation analysis.Results:(1) Compared with that in the HC group, ALFF was significantly enhanced in the right supplementary motor area, right hippocampus, left caudate nucleus, and right fusiform gyrus, and statistically attenuated in the left suboccipital gyrus in the OCD group ( P<0.05). (2) Compared with that in the HC group, effector connectivity from the right dorsolateral superior frontal gyrus to right supplementary motor area was significantly attenuated, and effector connectivity from the left superior occipital gyrus to right supplementary motor area was significantly enhanced in the OCD group ( P<0.05); compared with that in the HC group, effector connectivity from the right fusiform gyrus to right precentral gyrus was significantly attenuated, and effector connectivity from the right hippocampus to left mesial temporal gyrus was significantly enhanced in the OCD group ( P<0.05). (3) In OCD patients, altered ALFF in the left caudate nucleus was positively correlated with obsession scores ( r=0.357, P=0.027), and altered effector connectivity from the right dorsolateral superior frontal gyrus to right supplementary motor area was negatively correlated with obsession scores ( r=-0.312, P=0.029). Conclusion:Abnormalities in sensorimotor network function are closely related to clinical symptoms in patients with OCD.

Objective:To analyze brain topological property data through machine learning methods and explore changes in brain network topological properties in patients with schizophrenia.Methods:From January 2022 to August 2023, functional magnetic resonance imaging data of 60 patients with schizophrenia and 56 healthy controls were collected , and the data were preprocessed to construct brain functional networks and extract global and nodal topological properties. All subjects were divided into a training group and a testing group.The data of training group were fitted based on support vector machine, and the predictive performance was evaluated through cross-validation.The model was optimized by recursive feature elimination algorithm, then the indicators that contributed the most to predictive performance were extrated.The classification performance of the testing group was calculated based on the trained model with optimal predictive performance.SPSS 20.0 software was used for data analysis, the independent t-test and χ2 test were used for comparing the differences between the two groups. Results:The support vector machine achieved an accuracy of 75.00% in predicting the test group of schizophrenia patients based on all indicators. After removing redundant features and combining with the recursive feature elimination algorithm, the accuracy of the SVM model in predicting the test group increased to 90.00%. The nodal global efficiency(Ne)of the left superior temporal gyrus, right dorsal agranular insula, bilateral dorsal granular insula, bilateral caudal cingulate gyrus, and left lateral orbitofrontal cortex in the model contributed the most to classification.Compared to the control group, patients with schizophrenia had abnormal Ne values in these brain regions.Conclusion:There are multiple brain regions with abnormal Ne values in patients with schizophrenia, indicating that the abnormalities in information integration and transmission functions may be related to the imbalance in the dynamic equilibrium of the patients' brain networks.

Objective:To explore the value of nodal integrated topological attributes (NITA) based on machine learning model in identifying schizophrenia.Methods:A total of 56 patients with first-onset schizophrenia admitted to Department of Psychiatry, Second Affiliated Hospital of Xinxiang Medical University from January 2022 to August 2023 and 56 healthy volunteers recruited from community were selected. Functional MRI data were collected, and brain functional networks were constructed after preprocessing. Global and nodal topological attributes were extracted using graph theory as training features. Participants were divided into training set (46 schizophrenia patients and 46 heathy volunteers) and testing set (10 schizophrenia patients and 10 heathy volunteers). Random Forest Classifier (RFC), Support Vector Machine (SVM), and Gradient Boosting Tree (XGBoost) models were fitted to global and nodal topological attributes in the training set to calculate the accuracy, recall rate, F1 value, and area under receiver operating characteristic curve (AUC) of each model. Generalization ability was analyzed based on the performance of testing set, and excellent topological attributes were screened out. Selected topological attributes were reduced to one-dimensional features through principal component analysis,and then fitted to the above models, and feature-adapted model was selected based on the performances of training and testing sets. Statistical analysis of the new dimensional features of each brain region of schizophrenia patients and heathy volunteers was performed. Combined with false discovery rate (FDR), new dimension features with significant differences were selected and fitted with the adapted model.Results:In the training set, machine learning models using node topological attributes achieved higher accuracy, recall rate, F1 scores, and AUC compared with those using global topological attributes. In the test set, the SVM model using node topological attributes showed stable generalizability (accuracy=75.00%, recall rate=100.00%, F1 score=0.80, AUC=0.92). The node topological attribute metrics were down-dimensionally named NITA. Based on validation results of SVM model using NITA in the training set (accuracy of 77.00%, recall of 72.00%, F1 value of 0.76, AUC of 0.86) and performance in the testing set (accuracy of 66.67%, recall of 83.33%, F1 value of 0.71, AUC of 0.61), SVM was selected as the adapted model. NITA in the right middle frontal gyrus ventrolateral area, left inferior frontal gyrus dorsal area, right precentral gyrus caudal ventrolateral area, left superior temporal gyrus rostral area, right fusiform gyrus lateroventral area, right inferior parietal lobule rostrodorsal area, left occipital polar cortex showed significant difference between patients and volunteers ( P<0.05, FDR-corrected). The optimal model (FDR-PCAN-SVM) obtained via NITA being trained on corresponding brain area reached an accuracy of 93.74%, recall rate of 98.00%, F1 value of 0.94, and AUC of 0.96 in the training set and accuracy of 83.33%, recall rate of 66.67%, F1 value of 0.80, and AUC of 0.92 in the testing set. Conclusion:NITA may serve as a potential image biomarker for schizophrenia identification; brain regions with abnormal NITA is key nodes in information exchange and integration within the brain networks in schizophrenia patients.

Objective:To explore the abnormalities of efficiency in resting state functional brain network in patients with paranoid schizophrenia and the correlations between efficiencies and clinical symptoms.Methods:A total of 73 patients with schizophrenia (SZ group) met with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-Ⅳ) criteria for schizophrenia and 70 healthy controls (HC group) were included .All subjects were checked by using functional magnetic resonance imaging (fMRI), and positive and negative syndrome scale(PANSS) were used to assess the symptoms.Abnormalities of global and local efficiency of brain regions in brain functional network were analyzed by graph theory.Pearson correlation was used to analyze the correlation between the abnormal global efficiency and local efficiency of brain regions of SZ group and PANSS.SPSS 20.0 software was used for dependent-sample t-test, ANOVA test and Pearson correlation analysis. Results:Compared with the HC group, SZ group showed increased global efficiency in bilateral thalamus(left: 0.26±0.06, 0.28±0.04, t=2.03, P=0.044.right: 0.26±0.06, 0.28±0.05, t=2.08, P=0.040), right orbital part of middle frontal gyrus(0.21±0.04, 0.23±0.05, t=2.25, P=0.026), cerebellar lobule Ⅸ(0.19±0.06, 0.21±0.05, t=2.56, P=0.011) and vermis Ⅲ(0.15±0.08, 0.19±0.07, t=3.27, P=0.001), while decreased global efficiency in bilateral parahippocampal gyrus(left: 0.25±0.05, 0.22±0.05, t=-3.34, P=0.001.right: 0.27±0.04, 0.23±0.05, t=-4.96, P=0.000), superior occipital gyrus(left: 0.27±0.03, 0.26±0.03, t=-2.70, P=0.008.right: 0.27±0.02, 0.26±0.03, t=-2.73, P=0.007), superior parietal gyrus(left: 0.27±0.03, 0.26±0.05, t=-2.63, P=0.010.right: 0.27±0.03, 0.25±0.05, t=-2.76, P=0.007), paracentral lobule(left: 0.28±0.03, 0.26±0.07, t=-2.47, P=0.015.right: 0.28±0.04, 0.25±0.07, t=-3.06, P=0.003), left precental gyrus(0.28±0.04, 0.27±0.04, t=-1.98, P=0.049), left cuneus(0.26±0.04, 0.25±0.04, t=-2.08, P=0.039), left lingual gyrus(0.29±0.03, 0.28±0.03, t=-2.28, P=0.024), left middle occipital gyrus(0.29±0.03, 0.28±0.03; t=-2.74, P=0.007), left middle temporal gyrus(0.28±0.03, 0.26±0.03, t=-2.73, P=0.007), temporal pole in left middle temporal gyrus(0.20±0.06, 0.18±0.06, t=-2.59, P=0.011) and right hippocampus(0.27±0.04, 0.26±0.06, t=-2.05, P=0.042).Compared with the HC group, SZ group showed increased local efficiency in bilateral caudate nucleus(left: 0.33±0.06, 0.35±0.05, t=2.54, P=0.012.right: 0.33±0.07, 0.35±0.04, t=2.77, P=0.007) and left superior occipital gyrus(0.39±0.03, 0.40±0.02, t=2.17, P=0.031), while decreased local efficiency in bilateral parahippocampal gyrus(left: 0.35±0.04, 0.32±0.07, t=-3.16, P=0.002.right: 0.34±0.04, 0.32±0.07, t=-2.91, P=0.004), left supplementary motor area(0.36±0.02, 0.35±0.05, t=-2.01, P=0.047), left inferior parietal but supramarginal and angular gyrus(0.35±0.03, 0.34±0.05, t=-2.65, P=0.009), left cerebellar crus Ⅱ(0.37±0.03, 0.36±0.04, t=-2.01, P=0.046), lobule ⅦB(0.37±0.03, 0.35±0.07, t=-1.98, P=0.049), right posterior cingulate gyrus(0.36±0.04, 0.34±0.07, t=-2.07, P=0.041), right superior parietal gyrus(0.37±0.03, 0.36±0.05, t=-2.19, P=0.031), right precuneus(0.36±0.02, 0.35±0.04, t=-2.36, P=0.020), right paracentral lobule(0.37±0.02, 0.36±0.06, t=-2.07, P=0.041) and right temporal pole in middle temporal gyrus(0.33±0.08, 0.30±0.09, t=-2.09, P=0.038).The global efficiency of bilateral paracentral lobule and left temporal pole in middle temporal gyrus in SZ group were negatively correlated with the negative scale scores( r=-0.25, -0.25, -0.26, all P<0.05).The global efficiency of right hippocampus in SZ group was positively correlated with total scores of PANSS( r=0.23, P=0.049).The global efficiency of left middle temporal gyrus in SZ group was negatively correlated with total scores of PANSS( r=-0.23, P=0.049).The local efficiency of right paracentral lobule in SZ group was negatively correlated with the positive scale scores( r=-0.24, P=0.038). Conclusion:The brain networks of patients with first-episode paranoid schizophrenia may have regional dysfunction in the transmission efficiency and fault-tolerant ability of resting state brain functional network, and the abnormalities of efficiency may be associated with the severity of psychiatric symptoms in several brain regions.

Objective:To explore the functional connections of the whole brain and the two hemispheres in patients with obsessive-compulsive disorder (OCD).Methods:Twenty-six patients with obsessive-compulsive disorder(patients group) and thirty-seven healthy controls matched in gender, age and education(control group) were enrolled.All the participants accepted the resting-state functional magnetic resonance (rs-fMRI) scan.Based on DPABI and REST software, degree centrality (DC) and voxel - mirrored homotopic connectivity (VMHC) approaches were used to explore the pattern of functional connection in OCD.Results:Compared with the control group, the DC values in the right posterior cerebellar lobe(MNI: x, y, z=45, -87, -12), left precentral gyrus(MNI: x, y, z=-54, 9, 39), left inferior parietal lobule(MNI: x, y, z=-48, -51, 42), right anterior cingulate cortex(MNI: x, y, z=3, 18, 48) were significantly higher( t values were 5.75, 5.26, 5.28 and 5.16, respectively), and the DC values in the left inferior frontal gyrus(MNI: x, y, z=-36, 9, 30) were significantly lower( t value was -6.65) in patients group.The VMHC values in bilateral posterior cerebellar lobe(MNI: x, y, z=±51, -69, -33), bilateral inferior parietal lobule(MNI: x, y, z=±48, -51, 54), bilateral anterior cingulate cortex(MNI: x, y, z=±3, 21, 45)in patients group were significantly higher that those in control group( t values were 5.19, 5.19, 5.02, 5.02, 5.15 and 5.15, respectively). The DC and VMHC values in patients group were not significantly correlated with clinical symptoms(-0.23< r<0.19, P>0.05). Conclusion:OCD patients have abnormal connections between key brain network nodes and relevant brain regions, and functional connections have increased among multiple cerebral hemispheres.

Objective:To analyze the setup and residual errors of spinal cord during online CT-guided radiotherapy for patients with esophageal cancer, and to discuss the necessity of segmental extension of spinal cord.Methods:According to the radiotherapy site, 60 cases of esophageal cancer were divided into the neck, chest and abdomen groups, 20 cases in each group. Cervical pleura or vacuum bag was fixed, IMRT technology was adopted, and pre-treatment CT images were obtained by CT Vision, and 20 consecutive CT scans were collected for each case. CT images were imported into MIM software. The parameters of the setup errors were processed and extracted. The CT spinal cord was delineated for verification and planning, and the Dice coefficient, Hausdorff maximum distance and centroid coordinate of the delineated spinal cord were processed and extracted. Compatibility anova data were adopted. The calculation formula of the extension margin is M PRV= 1.3 ∑ total+ 0.5 σ total. Results:Residual centroid method was employed. Non-on-line and on-line CT-guided radiotherapy, the extension margins of neck, chest, abdominal spinal cord in the x-, y-and z-axis were 3.86, 5.37, 6.36 mm; 3.45, 3.83, 4.51 mm; 4.05, 4.83, 7.06 mm, vs, 2.85, 2.19, 2.83 mm; 2.32, 2.20, 2.16 mm; 2.86, 2.21, 2.83 mm, respectively. During residual Hausdorff distance method, non-on-line and on-line CT guided radiotherapy, the extension margins of neck, chest, abdominal spinal cord in the x-, y-and z-axis were 3.10, 5.33, 6.15 mm; 3.30, 3.77, 4.61 mm; 3.35, 4.76, 6.87 mm, vs, 2.12, 2.06, 2.32 mm; 2.12, 2.06, 2.32 mm; 2.12, 2.06, 2.32 mm, respectively.Conclusion:The setup errors and residual errors are different in each segment of spinal cord. Henc, different extension margins should be given.

Objective To investigate the spontaneous activity of brain neurons in patients with obsessive－compulsive disorder (OCD) under resting state. Methods Forty－eight OCD patients and 50 age－, gender－ and year of education－matched normal controls were enrolled. All the subjects underwent 3.0 T fMRI to acquire resting state brain image. The brain regions with significant differences in amplitude of low－frequency fluctuation (ALFF) between patients and controls were analyzed. Whole－brain functional connectivity (FC) were analyzed using the brain regions with significant differences as seed points, and the correlation between brain regions with significant differences in ALFF and FC analysis and obsessive－compulsive symptoms was analyzed. Results Compared to the control group, the ALFF of left dorsolateral prefrontal cortex increased in patients with OCD (t=4.305, P＜0.001). Compared to the controls, the analysis of whole－brain FC (based on MNI template) with the left dorsolateral prefrontal cortex as the regions of interest showed that the FC strength between the left dorsolateral prefrontal cortex and right orbital inferior frontal cortex (t=3.897, P＜0.001), left anterior cingulate cortex (t=3.370, P＜0.001), right anterior cingulate cortex (t=4.299, P＜0.001), left middle cingulate cortex (t=3.220, P＜0.001), right middle cingulate cortex (t=4.607, P＜0.001) enhanced; the FC strength between the left dorsolateral prefrontal cortex and the left opercular part of inferior frontal gyrus (t=－4.630, P＜0.001) weakened in patients with OCD. The FC between the left dorsolateral prefrontal cortex and the left opercular part of inferior frontal gyrus was negatively correlated with obsessions score(r=－0.369, P=0.014), compulsions score (r=－0.392, P=0.009) and total score (r=－0.393, P=0.008) of the Yale－Brown obsessive compulsive scale (Y－BOCS). Conclusion In patients with OCD, spontaneous neural activity of the left dorsolateral prefrontal cortex is enhanced in resting state, and the FC with multiple brain regions is abnormal. The FC strength between the left dorsolateral prefrontal cortex and the left opercular part of inferior frontal gyrus is associated with obsessive－compulsive symptoms.

Objective Regional homogeneity (ReHo) and functional connectivity (FC) were used to study obsessive-compulsive disorder(OCD),and to explore the mechanism of OCD in resting state.Method Resting-state functional magnetic resonance imaging (RS-fMRI) was performed in 55 patients with OCD (OCD group) and 50 normal controls (control group) matched by sex,age,nationality and education.The data and screening abnormal brain areas were analyzed and compared by DPARSFA2.3 and Rest software in OCD group.Whole brain FC analysis was performed with abnormal brain areas as seed points.Result Compared with the control group,ReHo in right thalamus (MNI:x=9,y=-24,z=6,t=4.3217) and left superior marginal gyrus (MNI:x =-45,y =-30,z =27,t =3.6320) increased and ReHo in right caudate nucleus (MNI:x=3,y=15,z=9,t=-3.1687) decreased in obsessive-compulsive disorder group,and the difference was statistically significant(P＜0.05).Using left superior marginal gyrus,fight thalamus and right caudate nucleus as seed voxels,the whole brain FC analysis showed that there were abnormal functional connections between bilateral cerebellar foot 1/2 area and left supramarginal gyrus,right thalamus and right caudate nucleus (P＜0.05) and the left supramarginal gyrus-bilateral cerebellum feet 1 area-right thalamic circuit and left supramarginal gyrus-bilateral cerebellum feet 1,2-right caudate nucleus-right thalamic circuit existed in 0CD group.Conclusion The left supramarginal gyrus-bilateral cerebellum feet 1 area-right thalamic circuit and left supramarginal gyrus-bilateral cerebellum feet 1,2-right caudate nucleus-right thalamic circuit may play an important role in the mechanism of OCD.

Adolescent ; Adult ; Brain ; abnormalities ; pathology ; physiopathology ; Child ; Female ; Humans ; Image Processing, Computer-Assisted ; Male ; Nerve Net ; pathology ; physiopathology ; Schizophrenia ; etiology ; physiopathology ; Schizophrenic Psychology

Objective To investigate the characteristics of amygdala neural circuitry in comorbidity of late-life depression (LLD) and cognitive impairment. Methods Twenty-four LLD,eighteen amnestic mild cognitive impairments (aMCI),thirteen aMCI with depression (dMCI) and thirty cognitive normal (CN) subjects completed resting-state functional magnetic resonance imaging scan. Main effects of depression and MCI and their interactions on the intrinsic amygdala functional connectivity network ( AFCN) connectivity were examined. Behavioral significance of AFCN that voxel-wised amygdala connectivity correlating with de-pression severity and memory scores were also tested after controlling the effects of covariates,including age, gender,education, gray matter atrophy, and group. Results The immediate memory and delayed memory function in the aMCI group (-0. 75 ± 0. 77 and -1. 13 ± 0. 56) and the dMCI group (-1. 07 ± 0. 79 and-1. 00±0. 52) were significantly lower than those of the CN group (0. 46±0. 73 and 0. 60±0. 61),and the difference was statistically significant (P<0. 01). Depression and anxiety in the LLD group (1. 00±0. 53 and 0. 93±0. 98) and the dMCI group (0. 86±0. 80 and 0. 78±0. 82) were significantly higher than those of the CN group (-0. 92±0. 25 and -0. 74±0. 22),and the difference was statistically significant (P<0. 01). Brain network analysis showed that separated neural circuits were implicated in the depression and cognitive im-pairment. Importantly,interactive effects of depression and MCI on the AFCN were also identified,especially in the bilateral somatomotor area,inferior parietal cortex/precuneus,posterior cingulate cortex,right medial prefrontal cortex/dorsolateral prefrontal cortex and hippocampus. Behavioral significance of AFCN also re-vealed the distinctive neural circuits involved in the depression severity and memory deficits,respectively. Conjunction analysis further identified the overlapped neural circuits associated with depression and memory deficits were primarily in the left DLPFC,insula,hippocampus,right inferior prefrontal cortex and dorsomedi-al prefrontal cortex. Conclusions Depression and cognitive impairment synergistically facilitate functional decoupling of AFCN and thus compromise the integrity of amygdala networks. Distinct depression-related or MCI-related neural constructs represent the characteristics of clinical phenotype of depression or MCI alone, while overlapped circuits probably reveal the neural basis of comorbidity of LLD and MCI.