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Background Postpartum depression (PPD) is a serious postpartum mental health problem worldwide. To date, minimal is known about the alteration of topographical organization in the brain structural covariance network of patients with PPD. This study investigates the brain structural covariance networks of patients with PPD by using graph theoretical analysis. Methods High-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women. Cortical thickness was extracted from 64 brain regions to construct the whole-brain structural covariance networks by calculating the Pearson correlation coefficients, and their topological properties (e.g., small-worldness, efficiency, and nodal centrality) were analyzed by using graph theory. Nonparametric permutation tests were further used for group comparisons of topological metrics. A node was set as a hub if its betweenness centrality (BC) was at least two standard deviations higher than the mean nodal centrality. Network-based statistic (NBS) was used to determine the connected subnetwork. Results The PPD and control groups showed small-worldness of group networks, but the small-world network was more evidently in the PPD group. Moreover, the PPD group showed increased network local efficiency and almost similar network global efficiency. However, the difference of the network metrics was not significant across the range of network densities. The hub nodes of the patients with PPD were right inferior parietal lobule (BC = 13.69) and right supramarginal gyrus (BC = 13.15), whereas those for the HCs were left cuneus (BC = 14.96), right caudal anterior-cingulate cortex (BC = 15.51), and right precuneus gyrus (BC = 15.74). NBS demonstrated two disrupted subnetworks that are present in PPD: the first subnetwork with decreased internodal connections is mainly involved in the cognitive-control network and visual network, and the second subnetwork with increased internodal connections is mainly involved in the default mode network, cognitive-control network and visual network. Conclusions This study demonstrates the alteration of topographical organization in the brain structural covariance network of patients with PPD, providing in sight on the notion that PPD could be characterized as a systems-level disorder.

Objective: This study aimed to establish a mammography-based radiomics model for predicting the risk of estrogen receptor (ER)-positive, lymph node (LN)-negative invasive breast cancer recurrence based on Oncotype DX and validated it by using multicenter data. Methods: A total of 304 potentially eligible patients with pre-operative mammography images and available Oncotype DX score were retrospectively enrolled from two hospitals. The patients were grouped as training set (168 patients), internal test set (72 patients), and external test set (64 patients). Radiomics features were extracted from the mammography images of each patient. Spearman correlation analysis, analysis of variance, and least absolute shrinkage and selection operator regression were performed to reduce the redundant features in the training set, and the least absolute shrinkage and selection operator algorithm was used to construct the radiomics signature based on selected features. Multivariate logistic regression was utilized to construct classification models that included radiomics signature and clinical risk factors to predict low vs intermediate and high recurrence risk of ER-positive, LN-negative invasive breast cancer in the training set. The models were evaluated with the receiver operating characteristic curve in the training set. The internal and external test sets were used to confirm the discriminatory power of the models. The clinical usefulness was evaluated by using decision curve analysis. Results: The radiomics signature consisting of three radiomics features achieved favorable prediction performance. The multivariate logistic regression model including radiomics signature and clinical risk factors (tumor grade and HER 2) showed good performance with areas under the curve of 0.92 (95% confidence interval [CI] 0.86 to 0.97), 0.88 (95% CI 0.75 to 1.00), and 0.84 (95% CI 0.69 to 0.99) in the training, internal and external test sets, respectively. The DCA indicated that when the threshold probability is ranges from 0.1 to 1.0, the radiomics model adds more net benefit than the “treat all” or “treat none” scheme in internal and external test sets. Conclusion: As a non-invasive pre-operative prediction tool, the mammography-based radiomics model incorporating radiomics and clinical factors show favorable predictive performance for predicting the risk of ER-positive, LN-negative invasive breast cancer recurrence based on Oncotype DX. Advances in knowledge: The mammography-based radiomics model incorporating radiomics and clinical factors shows favorable predictive performance for predicting the risk of ER-positive, LN-negative invasive breast cancer recurrence.

Pregnancy leads to long-lasting changes in human brain structure; however, little is known regarding alterations in the topological organization of functional networks. In this study, we investigated the effect of pregnancy on human brain function networks. Resting-state fMRI data was collected from eighteen primiparous mothers and twenty-four nulliparous control women of similar age, education level and body mass index (BMI). The functional brain network and topological properties were calculated by using GRETNA toolbox. The demographic data differences between two groups were computed by the independent two sample t-test. We tested group differences in network metrics' area under curve (AUC) using non-parametric permutation test of 1,000 permutations and corrected for false discovery rate (FDR). Differences in regional networks between groups were evaluated using non-parametric permutation tests by network-based statistical analysis (NBS). Compared with the nulliparous control women, a hub node changed from left inferior temporal gyrus to right precentral gyrus in primiparous mothers, while primiparous mothers showed enhanced network global efficiency (p = 0.247), enhanced local efficiency (p = 0.410), larger clustering coefficient (p = 0.410), but shorter characteristic path length (p = 0.247), smaller normalized clustering coefficient (p = 0.111), and shorter normalized characteristic path length (p = 0.705). Although both groups of functional networks have small-world property (σ 1), the σ values of primiparous mothers were decreased significantly. NBS evaluation showed the majority of altered connected sub-network in the primiparous mothers occurred in the bilateral frontal lobe gyrus (p 0.05). Altered functional network metrics and an abnormal sub-network were found in primiparous mothers, suggested that pregnancy may lead to changes in the brain functional network.

Postpartum depression (PPD) is a common mood disorder with increasing incidence year by year. However, the dynamic changes in local neural activity of patients with PPD remain unclear. In this study, we utilized the dynamic amplitude of low-frequency fluctuation (dALFF) method to investigate the abnormal temporal variability of local neural activity and its potential correlation with clinical severity in PPD.Twenty-four patients with PPD and nineteen healthy primiparous mothers controls (HCs) matched for age, education level and body mass index were examined by resting-state functional magnetic resonance imaging (rs-fMRI). A sliding-window method was used to assess the dALFF, and a k-means clustering method was used to identify dALFF states. Two-sample t-test was used to compare the differences of dALFF variability and state metrics between PPD and HCs. Pearson correlation analysis was used to analyze the relationship between dALFF variability, states metrics and clinical severity.(1) Patients with PPD had lower variance of dALFF than HCs in the cognitive control network, cerebellar network and sensorimotor network. (2) Four dALFF states were identified, and patients with PPD spent more time on state 2 than the other three states. The number of transitions between the four dALFF states increased in the patients compared with that in HCs. (3) Multiple dALFF states were found to be correlated with the severity of depression. The variance of dALFF in the right middle frontal gyrus was negatively correlated with the Edinburgh postnatal depression scale score.This study provides new insights into the brain dysfunction of PPD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding the neurophysiological mechanisms of PPD.

Background Postpartum depression (PPD) is a serious postpartum mental health problem worldwide. However, the cortical structural alterations in patients with PPD remain unclear. This study investigated the cortical structural alterations of PPD patients through multidimensional structural patterns and their potential correlations with clinical severity. Methods High-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women matched for age, educational level, and body mass index. The severity of PPD was assessed by using the Hamilton Depression Scale (HAMD) and Edinburgh Postnatal Depression Scale (EPDS) scores. Cortical morphological parameters including cortical thickness, surface area, and mean curvature were calculated using the surface-based morphometric (SBM) method. General linear model (GLM) analyses were performed to evaluate the relationship of cortical morphological parameters with clinical scales. Results In the present study, PPD patients showed a thinner cortical thickness in the right inferior parietal lobule compared with the healthy controls. Increased surface area was observed in the left superior frontal gyrus, caudal middle frontal gyrus, middle temporal gyrus, insula, and right supramarginal cortex in PPD patients. Likewise, PPD patients exhibited a higher mean curvature in the left superior and right inferior parietal lobule. Furthermore, increased cortical surface area in the left insula had a positive correlation with EPDS scores, and higher mean curvature in the left superior parietal lobule was negatively correlated with EPDS scores. Limitations First, SBM cannot reflect the changes of subcortical structures that are considered to play a role in the development of PPD. Second, the sample size of this study is small. These positive results should be interpreted with caution. Third, this cross-sectional study does not involve a comparison of structural MRI before and after pregnancy. Conclusions The complex cortical structural alterations of patients with PPD mainly involved the prefrontal and parietal regions. The morphometric alterations in these specific regions may provide promising markers for assessing the severity of PPD.

Background and purpose Different atrophy of hippocampus subregions is a valuable indicator of patients with Alzheimer’s disease (AD). To explore the relationship among the hippocampal subregions of patients with AD, altered gray matter structural covariance of hippocampal subregions in patients with AD was studied. Materials and methods Participants were selected from the Open Access Series of Imaging Studies Database. Pearson correlations among the volume of the hippocampal subregions were generated as structural covariance network. Topological metrics for all selected sparsity ranges were calculated in the healthy controls (HCs) and patients with AD by using the GRETNA software package. Spearman correlation analysis was performed to statistically analyze the volume and Mini-mental State Examination (MMSE) scores of the hippocampal subregions of the patients with AD, with age and gender as interference covariates and corrected for false discovery rate (FDR) (p Results The structural covariance network properties of the hippocampal subregions of patients with AD changed. The clustering coefficient (Cp) and network efficiency (Ne) decreased, characteristic path length (Lp) increased, and the hub nodes changed. The volumes of left parasubiculum , right granule cell layer of dentate gyrus (GC-DG), right molecular layer of the hippocampus (molecular_layer_HP), right Cornu Ammonis (CA) regions CA1 of the hippocampus proper, right fimbria and right CA4 were significantly correlated with the MMSE scores. Conclusions The structural covariance network of the hippocampal subregions of patients with AD was reorganized, and the transmission efficiency was weakened. This study explored the changes in these subregions from the network level, which may provide a new perspective and theoretical basis for the neurobiological mechanisms of patients with AD.

In patients with Alzheimer's Disease (AD), the hippocampal network has been extensively investigated in previous studies; however, little is known about the morphological network associated with the hippocampus in the AD patients. A total of 68 patients with AD and another 68 gender and age matched healthy subjects were studied. Individual-level morphological hippocampal networks were constructed based on volume and texture features extracted from MRI to study the connections between bilateral hippocampus and 11 other subcortical gray matter structures. The relationship between morphological connections and Mini-Mental State Examination (MMSE) scores was also studied. Connections between bilateral hippocampus and bilateral thalamus, bilateral putamen were significant differences between the AD patients and controls (p 0.05). There were significantly different in bilateral hippocampal connectivity, and for the left hippocampus, the connection to the right caudate were found to be statistically significant. The morphological connections between left hippocampus and bilateral thalamus (left: R = 0.371, p 0.001; right: R = 0.411, p 0.001), bilateral putamen (left: R = 0.383, p 0.001; right: R = 0.348, p 0.001), right hippocampus and bilateral thalamus (left: R = 0.370, p 0.001; right: R = 0.387, p 0.001), left putamen (R = 0.377, p 0.001) were significantly positively correlated with the MMSE scores. Similar patterns were observed for left and right hippocampal connectivity and the connections highly associated with MMSE scores were also within the abnormal connections in AD patients.