Paracetamol-induced Liver Injury in An Experimental Rat Model: Noninvasive Assessment using DKI and the Effect of Gadoxetate on DKI Parameters.

PURPOSE: To explore the potential of diffusion kurtosis imaging (DKI) for assessing the degree of liver injury in a paracetamol-induced rat model and to simultaneously investigate the effect of intravenous gadoxetate on DKI parameters. METHODS: Paracetamol was used to induce hepatoxicity in 39 rats. The rats were pathologically classified into 3 groups: normal (n=11), mild necrosis (n=18), and moderate necrosis (n=10). DKI was performed before and, 15 min, 25 min, and 45 min after gadoxetate administration. Repeated-measures ANOVA with Tukey's multiple comparison test was used to investigate the effect of gadoxetate on mean diffusivity (MD) and mean diffusion kurtosis (MK) and to assess the differences in MD and MK among the three groups. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of the MD values when discriminating between the necrotic groups. RESULTS: Gadoxetate had no significant effect on either the MD or the MK, and the effect size was small. The MD in the moderate necrosis group was significantly lower than that in the other two groups (F = 13.502, p < 0.001; η2 = 0.428 [95% CI: 0.082-0.637]), while the MK did not significantly differ among the three groups (F = 2.702, p = 0.081; η2 = 0.131 [95% CI: 0.001-0.4003]). The AUCs of MD for discriminating the moderate necrosis or normal group from the other groups were 0.921 (95% CI: 0.832-1.000) and 0.831 (95% CI: 0.701-0.961), respectively. CONCLUSION: It would be better to measure the MD and MK before gadoxetate injection. MD showed potential for assessing the degree of liver necrosis in a paracetamol-induced liver injury rat model.

Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI.

PURPOSE: To determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm. METHODS: Patients diagnosed with HCC who underwent locoregional therapy were retrospectively collected. We constructed a deep learning-based subtraction model and automatically generated arterial subtraction images. Two radiologists evaluated LR-TR category on ordinary images and then evaluated again on ordinary images plus arterial subtraction images after a 2-month washout period. The reference standard for viability was tumor stain on the digital subtraction hepatic angiography within 1 month after MRI. RESULTS: 286 observations of 105 patients were ultimately enrolled. 157 observations were viable and 129 observations were nonviable according to the reference standard. The sensitivity and accuracy of LR-TR algorithm for detecting viable HCC significantly increased with the application of arterial subtraction images (87.9% vs. 67.5%, p < 0.001; 86.4% vs. 75.9%, p < 0.001). And the specificity slightly decreased without significant difference when the arterial subtraction images were added (84.5% vs. 86.0%, p = 0.687). The AUC of LR-TR algorithm significantly increased with the addition of arterial subtraction images (0.862 vs. 0.768, p < 0.001). The arterial subtraction images also improved inter-reader agreement (0.857 vs. 0.727). CONCLUSION: Extended application of deep learning-based arterial subtraction images on extracellular agents-enhanced MRI can increase the sensitivity of LR-TR algorithm for detecting viable HCC without significant change in specificity.

Computed tomography-based body composition parameters can predict short-term prognosis in ulcerative colitis patients.

OBJECTIVES: Emerging evidence suggests a potential relationship between body composition and short-term prognosis of ulcerative colitis (UC). Early and accurate assessment of rapid remission based on conventional therapy via abdominal computed tomography (CT) images has rarely been investigated. This study aimed to build a prediction model using CT-based body composition parameters for UC risk stratification. METHODS: In total, 138 patients with abdominal CT images were enrolled. Eleven quantitative parameters related to body composition involving skeletal muscle mass, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were measured and calculated using a semi-automated segmentation method. A prediction model was established with significant parameters using a multivariable logistic regression. The receiver operating characteristic (ROC) curve was plotted to evaluate prediction performance. Subgroup analyses were implemented to evaluate the diagnostic efficiency of the prediction model between different disease locations, centers, and CT scanners. The Delong test was used for statistical comparison of ROC curves. RESULTS: VAT density, SAT density, gender, and visceral obesity were significantly statistically different between remission and invalidation groups (all p < 0.05). The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of the prediction model were 82.61%, 95.45%, 69.89%, and 0.855 (0.792-0.917), respectively. The positive predictive value and negative predictive value were 70.79% and 93.88%, respectively. No significant differences in the AUC of the prediction model were found in different subgroups (all p > 0.05). CONCLUSIONS: The predicting model constructed with CT-based body composition parameters is a potential non-invasive approach for short-term prognosis identification and risk stratification. Additionally, VAT density was an independent predictor for escalating therapeutic regimens in UC cohorts. CRITICAL RELEVANCE STATEMENT: The CT images were used for evaluating body composition and risk stratification of ulcerative colitis patients, and a potential non-invasive prediction model was constructed to identify non-responders with conventional therapy for making therapeutic regimens timely and accurately. KEY POINTS: • CT-based prediction models help divide patients into invalidation and remission groups in UC. • Results of the subgroup analysis confirmed the stability of the prediction model with a high AUC (all > 0.820). • The visceral adipose tissue density was an independent predictor of bad short-term prognosis in UC.

The timing phase affected the inconsistency of APHE subtypes of liver observations in patients at risk for HCC on the multi-hepatic arterial phase imaging.

OBJECTIVE: To investigate whether liver observations in patients at risk for hepatocellular carcinoma (HCC) display inconsistent arterial phase hyperenhancement (APHE) subtypes on the multi-hepatic arterial phase imaging (mHAP) and to further investigate factors affecting inconsistent APHE subtype of observations on mHAP imaging. METHODS: From April 2018 to June 2021, a total of 141 patients at high risk of HCC with 238 liver observations who underwent mHAP MRI acquisitions were consecutively included in this retrospective study. Two experienced radiologists reviewed individual arterial phase imaging independently and assessed the enhancement pattern of each liver observation according to LI-RADS. Another two experienced radiologists identified and recorded the genuine timing phase of each phase independently. When a disagreement appeared between the two radiologists, another expert participated in the discussion to get a final decision. A separate descriptive analysis was used for all observations scored APHE by the radiologists. The Kappa coefficient was used to determine the agreement between the two radiologists. Univariate analysis was performed to investigate the factors affecting inconsistent APHE subtype of liver observations on mHAP imaging. RESULTS: The interobserver agreement was substantial to almost perfect agreement on the assessment of timing phase (κ = 0.712-0.887) and evaluation of APHE subtype (κ = 0.795-0.901). A total of 87.8% (209/238) of the observations showed consistent nonrim APHE and 10.2% (24/238) of the observations showed consistent rim APHE on mHAP imaging. A total of 2.1% (5/238) of the liver observations were considered inconsistent APHE subtypes, and all progressed nonrim to rim on mHAP imaging. 87.9% (124/141) of the mHAP acquisitions were all arterial phases and 12.1% (17/141) of the mHAP acquisitions obtained both the arterial phase and portal venous phase. Univariate analysis was performed and found that the timing phase of mHAP imaging affected the consistency of APHE subtype of liver observations. When considering the timing phase and excluding the portal venous phase acquired by mHAP imaging, none of the liver observations showed inconsistent APHE subtypes on mHAP imaging. CONCLUSION: The timing phase which mHAP acquisition contained portal venous phase affected the inconsistency of APHE subtype of liver observations on mHAP imaging. When evaluating the APHE subtype of liver observations, it's necessary to assess the timing of each phase acquired by the mHAP technique at first.

Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study.

PURPOSE: To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre. METHOD: A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre. RESULTS: Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774-0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700-0.888) and 0.883 (95% CI: 0.807-0.959), respectively. No statistical difference in AUC values among training set and testing sets was found. CONCLUSION: The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.

[Screen of key characteristic genes of nasopharyngeal carcinoma (NPC) base on machine learning and analysis of their correlation with immune cells].

Objective Machine learning was used to screen the key characteristic genes of nasopharyngeal carcinoma (NPC) and analyze their correlation with immune cells. Methods Download the NPC training datasets (GSE12452 and GSE13597) and the validation dataset (GSE53819) from the Gene Expression Omnibus (GEO). Firstly, the training data sets were merged and screened for differentially expressed genes (DEGs); Secondly, the DEGs were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Next, the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) algorithms were used to identify NPC-related genes in the training datasets and examined in the validation dataset, to further identify key genes using the area under curve (AUC) of receiver operating characteristic curve (ROC); Finally, the correlation between the key genes and immune cells was analyzed. Results A total of 55 DEGs were obtained, including 43 down-regulated genes and 12 up-regulated genes. The GO functions were enriched in humoral immune response, cell differentiation, neutrophil activation and chemokine receptor binding. The KEGG were mainly enriched in the IL-17 signaling pathway. The GSEA was enriched in cell cycle, extracellular matrix receptor interactions, cancer pathways and DNA replication. Immune infiltration analysis showed that the expression of naive B cells, memory B cells, and resting memory CD4+ T cells was significantly lower in NPC, while CD8+ T cells, naive CD4+ T cells, activated memory CD4+ T cells, follicular helper T cells, M0 macrophages and M1 macrophages were highly expressed in NPC. Among the feature genes screened by LASSO and SVM, only CCDC19, LAMB1, SPAG6 and RAD51AP1 genes' AUC were greater than 0.9 in both the training and validation datasets and were closely associated with immune cell infiltration. Conclusion The key genes CCDC19, LAMB1, SPAG6 and RAD51AP1 in NPC development are screened by machine learning algorithms, and are closely associated with immune cell infiltration.

Corrigendum: Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study.

[This corrects the article DOI: 10.3389/fcvm.2021.707508.].

Diagnostic Accuracy of Subtraction Coronary CT Angiography in Severely Calcified Segments: Comparison Between Readers With Different Levels of Experience.

Purpose: Subtraction coronary CT angiography (CCTA) may reduce blooming and beam-hardening artifacts. This study aimed to assess its value in improving the diagnostic accuracy of readers with different experience levels. Method: We prospectively enrolled patients with target segment who underwent CCTA and invasive coronary angiography (ICA). Target segment images were independently evaluated by three groups of radiologists with different experience levels with CCTA using ICA as the standard reference. Diagnostic accuracy was measured by the area under the curve (AUC), using ≥50% stenosis as the cut-off value. Results: In total, 134 target segments with severe calcification from 47 patients were analyzed. The mean specificity of conventional CCTA for each group ranged from 22.4 to 42.2%, which significantly improved with subtraction CCTA, ranging from 81.3 to 85.7% (all p < 0.001). The mean sensitivity of conventional CCTA for each group ranged from 83.3 to 88.0%. Following calcification subtraction, the mean sensitivity decreased for the novice (p < 0.001) and junior (p = 0.017) radiologists but was unchanged for the senior radiologists (p = 0.690). With subtraction CCTA, the mean AUCs of CCTA significantly increased: values ranged from 0.53, 0.54, and 0.61 to 0.70, 0.74, and 0.85 for the novice, junior, and senior groups (all p < 0.001). Conclusion: Subtraction CCTA could improve the diagnostic accuracy of radiologists at all experience levels of CCTA interpretation.

Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network.

OBJECTIVE: We aim to develop and validate a three-dimensional convolutional neural network (3D-CNN) model for automatic liver segment segmentation on MRI images. METHODS: This retrospective study evaluated an automated method using a deep neural network that was trained, validated, and tested with 367, 157, and 158 portal venous phase MR images, respectively. The Dice similarity coefficient (DSC), mean surface distance (MSD), Hausdorff distance (HD), and volume ratio (RV) were used to quantitatively measure the accuracy of segmentation. The time consumed for model and manual segmentation was also compared. In addition, the model was applied to 100 consecutive cases from real clinical scenario for a qualitative evaluation and indirect evaluation. RESULTS: In quantitative evaluation, the model achieved high accuracy for DSC, MSD, HD and RV (0.920, 3.34, 3.61 and 1.01, respectively). Compared to manual segmentation, the automated method reduced the segmentation time from 26 min to 8 s. In qualitative evaluation, the segmentation quality was rated as good in 79% of the cases, moderate in 15% and poor in 6%. In indirect evaluation, 93.4% (99/106) of lesions could be assigned to the correct segment by only referring to the results from automated segmentation. CONCLUSION: The proposed model may serve as an effective tool for automated anatomical region annotation of the liver on MRI images.

Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience.

BACKGROUND: To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists. METHODS: We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1-Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard. Reader 3 and Reader 4 interpreted with AI system assistance, and the other four readers interpreted without the AI system. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy (area under the receiver operating characteristic curve (AUC)) of the six readers were calculated at the patient and vessel levels. Additionally, we evaluated the interobserver consistency between Reader 1 and Reader 2, Reader 3 and Reader 4, and Reader 5 and Reader 6. RESULTS: The AI system had 94% and 78% sensitivity at the patient and vessel levels, respectively, which were higher than that of Reader 5 and Reader 6. AI-assisted Reader 3 and Reader 4 had higher sensitivity (range + 7.2-+ 16.6% and + 5.9-+ 16.1%, respectively) and NPVs (range + 3.7-+ 13.4% and + 2.7-+ 4.2%, respectively) than Reader 1 and Reader 2 without AI. Good interobserver consistency was found between Reader 3 and Reader 4 in interpreting ≥ 50% stenosis (Kappa value = 0.75 and 0.80 at the patient and vessel levels, respectively). Only Reader 1 and Reader 2 showed poor interobserver consistency (Kappa value = 0.25 and 0.37). Reader 5 and Reader 6 showed moderate agreement (Kappa value = 0.55 and 0.61). CONCLUSIONS: Our study showed that using AI could effectively increase the sensitivity of inexperienced readers and significantly improve the consistency of coronary stenosis diagnosis via CCTA. Trial registration Clinical trial registration number: ChiCTR1900021867. Name of registry: Diagnostic performance of artificial intelligence-assisted coronary computed tomography angiography for the assessment of coronary atherosclerotic stenosis.

Impact of Several Factors on the Diagnostic Interpretability of Coronary Computed Tomographic Angiography Using a 256-Detector Row CT Scanner.

OBJECTIVE: This study aimed at exploring the impact of patient-related, vessel-related, image quality-related and cardiovascular risk factors on coronary computed tomographic angiography (CCTA) interpretability using 256-detector row computed tomography (CT). METHODS: One hundred ten patients who underwent CCTA and Invasive Coronary Angiography (ICA) were consecutively, retrospectively enrolled from January 2018 to October 2018. Using ICA as the reference standard, ≥50% diameter stenosis was defined as the cut-off criterion to detect the diagnostic performance of CCTA. Diagnostic reproducibility was investigated by calculating the interrater reproducibility of CCTA. Multiple logistic regression models were performed to evaluate the impact of 14 objective factors. RESULTS: A total of 1019 segments were evaluated. The per-segment sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CCTA were 76.8%, 93.7%, 91.2%, 67.8%, and 95.9%, respectively. The per-segment diagnostic reproducibility was 0.44 for CCTA. Regarding accuracy, a negative association was found for stenosis severity, calcium load, and hyperlipidaemia. Regarding sensitivity, calcium load and diabetes mellitus (DM) were positively related. Regarding specificity, a negative correlation was observed between stenosis severity and calcium load. Regarding interrater reproducibility, stenosis severity and calcium load were negatively associated, whereas male sex and the signal-to-noise ratio (SNR) were positively related (all p<0.05). CONCLUSION: Per-segment 256-detector row CCTA performance was optimal in stenosis-free or occluded segments. Heavier calcium load was associated with poorer CCTA interpretability. On the one hand, our findings confirmed the rule-out value of CCTA; on the other hand, improvements in calcium subtractions and deep learning-based tools are suggested to improve CCTA diagnostic interpretability.

Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study.

Aims: In this retrospective, multi-center study, we aimed to estimate the diagnostic accuracy and generalizability of an established deep learning (DL)-based fully automated algorithm in detecting coronary stenosis on coronary computed tomography angiography (CCTA). Methods and results: A total of 527 patients (33.0% female, mean age: 62.2 ± 10.2 years) with suspected coronary artery disease (CAD) who underwent CCTA and invasive coronary angiography (ICA) were enrolled from 27 hospitals from January 2016 to August 2019. Using ICA as a standard reference, the diagnostic accuracy of the DL algorithm in the detection of ≥50% stenosis was compared to that of expert readers. In the vessel-based evaluation, the DL algorithm had a higher sensitivity (65.7%) and negative predictive value (NPV) (78.8%) and a significantly higher area under the curve (AUC) (0.83, p < 0.001). In the patient-based evaluation, the DL algorithm achieved a higher sensitivity (90.0%), NPV (52.2%) and AUC (0.81). Generalizability analysis of the DL algorithm was conducted by comparing its diagnostic performance in subgroups stratified by sex, age, geographic area and CT scanner type. The AUCs of the DL algorithm in the aforementioned subgroups ranged from 0.79 to 0.86 and from 0.75 to 0.93 in the vessel-based and patient-based evaluations, both without significant group differences (p > 0.05). The DL algorithm significantly reduced post-processing time (160 [IQR:139-192] seconds), in comparison to manual work (p < 0.001). Conclusions: The DL algorithm performed no inferior to expert readers in CAD diagnosis on CCTA and had good generalizability and time efficiency.

Subtraction improves the accuracy of coronary CT angiography for detecting obstructive disease in severely calcified segments.

OBJECTIVE: To investigate the accuracy, diagnostic confidence, and interobserver agreement of subtraction coronary CT angiography (CCTA) versus invasive coronary angiography on 320-row CT in coronary segments with severe or non-severe calcification. MATERIALS/METHODS: Sixty-four patients (33 men, 66.6 ± 8.2 years) with suspected coronary artery disease (CAD) were prospectively enrolled from October 2019 to June 2020. The cross-sectional circumferential extent of calcification was used to classify calcified segments as non-severely ( < 180°) or severely calcified ( ≥ 180°). Three independent, blinded radiologists evaluated the severity of coronary stenosis. Interobserver agreement was evaluated using Fleiss' kappa (κ). A multiple-reader multiple-case receiver operating characteristic (ROC) method was conducted, and diagnostic accuracy was measured using the mean areas under the ROC curves (AUCs), with ≥ 50% stenosis as a cut-off. Diagnostic confidence, diagnostic accuracy, and interobserver agreement were compared between CCTA with or without subtraction information in severely and non-severely calcified segments. RESULTS: In cases with severe calcification (51 patients, 146 segments), CCTA with subtraction information achieved better diagnostic accuracy (per-patient AUC: 0.73 vs 0.57, p = 0.03; per-segment AUC: 0.85 vs 0.62, p = 0.01), diagnostic confidence (3.7 vs 2.6, p < 0.001), and interobserver agreement (κ: 0.59 vs 0.30). Diagnostic accuracy (per-patient AUC: 0.81 vs 0.93, p = 0.30; per-patient AUC: 0.79 vs 0.82, p = 0.54) was not increased in cases with non-severe calcification (13 patients, 190 segments). CONCLUSIONS: CCTA with subtraction information achieved better diagnostic accuracy in cases of severe calcification (circumferential extent ≥ 180°). However, for non-severe calcification (circumferential extent < 180°), the effect of calcium subtraction was unclear, as it did not improve diagnostic accuracy. KEY POINTS: • Subtraction coronary CT angiography achieves better diagnostic accuracy, higher diagnostic confidence, and increased interobserver agreement for severe calcification (circumferential extent ≥ 180°). • Calcium subtraction does not improve the diagnostic accuracy of coronary CT angiography for calcification with a circumferential extent of < 180°.

Prefrontal Granule Cell-Related Genes and Schizophrenia.

Although both the granular layer of the prefrontal cortex (PFC) and schizophrenia are unique in primates, especially humans, their linkage is unclear. Here, we tested whether schizophrenia is associated with expression profiles of the granule cell (GC)-related genes in the human PFC. We identified 14 candidate GC-related genes with gradually increased expression levels along the gradient of the agranular, dysgranular, light-granular, and granular prefrontal regions based on the densely sampled gene expression data of 6 postmortem human brains, and with more than 10-fold expression in neurons than other cell types based on the single-cell RNA-sequencing data of the human PFC. These GC-related genes were functionally associated with synaptic transmission and cell development and differentiation. The identified 14 GC-related genes were significantly enriched for schizophrenia, but not for depression and bipolar disorder. The expression levels of the 4 stable schizophrenia- and GC-related genes were spatially correlated with gray matter volume differences in the PFC between patients with schizophrenia and healthy controls. This study provides a set of candidate genes for the human prefrontal GCs and links expression profiles of the GC-related genes to the prefrontal structural impairments in schizophrenia.

Differential Reorganization of SMA Subregions After Stroke: A Subregional Level Resting-State Functional Connectivity Study.

Background and Purpose: The human supplementary motor area (SMA) contains two functional subregions of the SMA proper and preSMA; however, the reorganization patterns of the two SMA subregions after stroke remain uncertain. Meanwhile, a focal subcortical lesion may affect the overall functional reorganization of brain networks. We sought to identify the differential reorganization of the SMA subregions after subcortical stroke using the resting-state functional connectivity (rsFC) analysis. Methods: Resting-state functional MRI was conducted in 25 patients with chronic capsular stroke exhibiting well-recovered global motor function (Fugl-Meyer score >90). The SMA proper and preSMA were identified by the rsFC-based parcellation, and the rsFCs of each SMA subregion were compared between stroke patients and healthy controls. Results: Despite common rsFC with the fronto-insular cortex (FIC), the SMA proper and preSMA were mainly correlated with the sensorimotor areas and cognitive-related regions, respectively. In stroke patients, the SMA proper and preSMA exhibited completely different functional reorganization patterns: the former showed increased rsFCs with the primary sensorimotor area and caudal cingulate motor area (CMA) of the motor execution network, whereas the latter showed increased rsFC with the rostral CMA of the motor control network. Both of the two SMA subregions showed decreased rsFC with the FIC in stroke patients; the preSMA additionally showed decreased rsFC with the prefrontal cortex (PFC). Conclusion: Although both SMA subregions exhibit functional disconnection with the cognitive-related areas, the SMA proper is implicated in the functional reorganization within the motor execution network, whereas the preSMA is involved in the functional reorganization within the motor control network in stroke patients.

Altered Coupling Between Resting-State Cerebral Blood Flow and Functional Connectivity in Schizophrenia.

Background: Respective changes in resting-state cerebral blood flow (CBF) and functional connectivity in schizophrenia have been reported. However, their coupling alterations in schizophrenia remain largely unknown. Methods: 89 schizophrenia patients and 90 sex- and age-matched healthy controls underwent resting-state functional MRI to calculate functional connectivity strength (FCS) and arterial spin labeling imaging to compute CBF. The CBF-FCS coupling of the whole gray matter and the CBF/FCS ratio (the amount of blood supply per unit of connectivity strength) of each voxel were compared between the 2 groups. Results: Whole gray matter CBF-FCS coupling was decreased in schizophrenia patients relative to healthy controls. In schizophrenia patients, the decreased CBF/FCS ratio was predominantly located in cognitive- and emotional-related brain regions, including the dorsolateral prefrontal cortex, insula, hippocampus and thalamus, whereas an increased CBF/FCS ratio was mainly identified in the sensorimotor regions, including the putamen, and sensorimotor, mid-cingulate and visual cortices. Conclusion: These findings suggest that the neurovascular decoupling in the brain may be a possible neuropathological mechanism of schizophrenia.

Combination of volume and perfusion parameters reveals different types of grey matter changes in schizophrenia.

Diverse brain structural and functional changes have been reported in schizophrenia. Identifying different types of brain changes may help to understand the neural mechanisms and to develop reliable biomarkers in schizophrenia. We aimed to categorize different grey matter changes in schizophrenia based on grey matter volume (GMV) and cerebral blood flow (CBF). Structural and perfusion magnetic resonance imaging data were acquired in 100 schizophrenia patients and 95 healthy comparison subjects. Voxel-based GMV comparison was used to show structural changes, CBF analysis was used to demonstrate functional changes. We identified three types of grey matter changes in schizophrenia: structural and functional impairments in the anterior cingulate cortex and insular cortex, displaying reduction in both GMV and CBF; structural impairment with preserved function in the frontal and temporal cortices, demonstrating decreased GMV with normal CBF; pure functional abnormality in the anterior cingulate cortex and lateral prefrontal cortex and putamen, showing altered CBF with normal GMV. By combination of GMV and CBF, we identified three types of grey matter changes in schizophrenia. These findings may help to understand the complex manifestations and to develop reliable biomarkers in schizophrenia.

Modulation of APOE and SORL1 genes on hippocampal functional connectivity in healthy young adults.

Apolipoprotein E (APOE) and sortilin-related receptor (SORL1) genes act on the same metabolic pathway and have been associated with Alzheimer's disease (AD) characterized by hippocampal impairment. Although the effects of APOE on hippocampal resting-state functional connectivity (rsFC) have been reported, the main effects of SORL1 and SORL1 × APOE interactions on hippocampal rsFC in healthy subjects remain largely unknown. Here, we systematically investigated the main effects of SORL1 rs2070045, and APOE, and their interaction effects on hippocampal rsFC in healthy young adults. The main effect of APOE showed that risk ε4 carriers had decreased positive hippocampal rsFC with the precuneus/posterior cingulate cortex and subgenual anterior cingulate cortex, and increased positive hippocampal rsFC with the sensorimotor cortex compared with non-ε4 carriers. The main effect of SORL1 showed that risk G-allele carriers had decreased positive rsFC between the hippocampus and middle temporal gyrus compared with TT carriers. No significant additive interaction was observed. Instead, significant SORL1 × APOE non-additive interaction was found in negative rsFC between the hippocampus and inferior frontal gyrus. Compared with subjects with TT genotype, SORL1 G-allele carriers had a stronger negative rsFC in APOE ε4 carriers, but a weaker negative rsFC in APOE non-ε4 carriers. These findings suggest that SORL1 and APOE genes modulate different hippocampal rsFCs and have a complex interaction. The SORL1- and APOE-dependent hippocampal connectivity changes may at least partly account for their association with AD.