Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

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 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 explore the functional changes of brain network node information integration and separation in patients with obsessive-compulsive disorder (OCD) and its correlation with clinical symptoms.Methods:Fifty-six patients with obsessive-compulsive disorder (OCD group) and fifty-six healthy controls (control group) matched in gender, age and years of education were enrolled. All participants accepted resting-state functional magnetic resonance imaging scans of the whole brain. Yale-Brown Obsessive Compulsive Scale(Y-BOCS) was used to assess patients′ clinical symptoms. Based on graph theory and independent-sample t-test, the differences in functional network topological properties of nodes between the two groups were analyzed. Partial correlation analysis was used to discuss the relationship between the values of these properties and clinical symptoms. Results:Compared with the control group, the OCD group showed decreased global efficiency and increased shortest path length of the left superior temporal gyrus rostral area and right posterior central gyrus trunk region(both P<0.001, FDR corrected); decreased local efficiency( P=0.002, uncorrected) and clustering coefficient( P<0.001, FDR corrected) of the left inferior frontal gyrus dorsal region. Further analysis showed that the score of the global efficiency of the left superior temporal gyrus rostral area was positively correlated with the score of obsessive thoughts sub-scale ( r=0.390, P=0.005). The score of the shortest path length of the left superior temporal gyrus rostral area was negatively correlated with the obsessive thoughts sub-scale ( r=-0.359, P=0.010) in the OCD group. Conclusion:OCD patients have abnormal information integration and separation functions in the frontoparietal network(inferior frontal gyrus, posterior central gyrus) and abnormal information integration functions in the superior temporal gyrus rostral area. Higher global efficiency and shorter the shortest path length of the superior temporal gyrus rostral area suggest more obsessive thoughts in OCD patients.

Objective:To explore the functional changes of brain network node information integration and separation in patients with obsessive-compulsive disorder (OCD) and its correlation with clinical symptoms.Methods:Fifty-six patients with obsessive-compulsive disorder (OCD group) and fifty-six healthy controls (control group) matched in gender, age and years of education were enrolled. All participants accepted resting-state functional magnetic resonance imaging scans of the whole brain. Yale-Brown Obsessive Compulsive Scale(Y-BOCS) was used to assess patients′ clinical symptoms. Based on graph theory and independent-sample t-test, the differences in functional network topological properties of nodes between the two groups were analyzed. Partial correlation analysis was used to discuss the relationship between the values of these properties and clinical symptoms. Results:Compared with the control group, the OCD group showed decreased global efficiency and increased shortest path length of the left superior temporal gyrus rostral area and right posterior central gyrus trunk region(both P<0.001, FDR corrected); decreased local efficiency( P=0.002, uncorrected) and clustering coefficient( P<0.001, FDR corrected) of the left inferior frontal gyrus dorsal region. Further analysis showed that the score of the global efficiency of the left superior temporal gyrus rostral area was positively correlated with the score of obsessive thoughts sub-scale ( r=0.390, P=0.005). The score of the shortest path length of the left superior temporal gyrus rostral area was negatively correlated with the obsessive thoughts sub-scale ( r=-0.359, P=0.010) in the OCD group. Conclusion:OCD patients have abnormal information integration and separation functions in the frontoparietal network(inferior frontal gyrus, posterior central gyrus) and abnormal information integration functions in the superior temporal gyrus rostral area. Higher global efficiency and shorter the shortest path length of the superior temporal gyrus rostral area suggest more obsessive thoughts in OCD 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.