Neurovascular decoupling measured with quantitative susceptibility mapping is associated with cognitive decline in patients with type 2 diabetes.

Disturbance of neurovascular coupling (NVC) is suggested to be one potential mechanism in type 2 diabetes mellitus (T2DM) associated mild cognitive impairment (MCI). However, NVC evidence derived from functional magnetic resonance imaging ignores the relationship of neuronal activity with vascular injury. Twenty-seven T2DM patients without MCI and thirty healthy controls were prospectively enrolled. Brain regions with changed susceptibility detected by quantitative susceptibility mapping (QSM) were used as seeds for functional connectivity (FC) analysis. NVC coefficients were estimated using combined degree centrality (DC) with susceptibility or cerebral blood flow (CBF). Partial correlations between neuroimaging indicators and cognitive decline were investigated. In T2DM group, higher susceptibility values in right hippocampal gyrus (R.PHG) were found and were negatively correlated with Naming Ability of Montreal Cognitive Assessment. FC increased remarkably between R.PHG and right middle temporal gyrus (R.MTG), right calcarine gyrus (R.CAL). Both NVC coefficients (DC-QSM and DC-CBF) reduced in R.PHG and increased in R.MTG and R.CAL. Both NVC coefficients in R.PHG and R.MTG increased with the improvement of cognitive ability, especially for executive function. These demonstrated that QSM and DC-QSM coefficients can be promising biomarkers for early evaluation of cognitive decline in T2DM patients and help to better understand the mechanism of NVC.

Functional to structural plasticity in unilateral sudden sensorineural hearing loss: neuroimaging evidence.

A cortical plasticity after long-duration single side deafness (SSD) is advocated with neuroimaging evidence while little is known about the short-duration SSDs. In this case-cohort study, we recruited unilateral sudden sensorineural hearing loss (SSNHL) patients and age-, gender-matched health controls (HC), followed by comprehensive neuroimaging analyses. The primary outcome measures were temporal alterations of varied dynamic functional network connectivity (dFNC) states, neurovascular coupling (NVC) and brain region volume at different stages of SSNHL. The secondary outcome measures were pure-tone audiograms of SSNHL patients before and after treatment. A total of 38 SSNHL patients (21 [55%] male; mean [standard deviation] age, 45.05 [15.83] years) and 44 HC (28 [64%] male; mean [standard deviation] age, 43.55 [12.80] years) were enrolled. SSNHL patients were categorized into subgroups based on the time from disease onset to the initial magnetic resonance imaging scan: early- (n = 16; 1-6 days), intermediate- (n = 9; 7-13 days), and late- stage (n = 13; 14-30 days) groups. We first identified slow state transitions between varied dFNC states at early-stage SSNHL, then revealed the decreased NVC restricted to the auditory cortex at the intermediate- and late-stage SSNHL. Finally, a significantly decreased volume of the left medial superior frontal gyrus (SFGmed) was observed only in the late-stage SSNHL cohort. Furthermore, the volume of the left SFGmed is robustly correlated with both disease duration and patient prognosis. Our study offered neuroimaging evidence for the evolvement from functional to structural brain alterations of SSNHL patients with disease duration less than 1 month, which may explain, from a neuroimaging perspective, why early-stage SSNHL patients have better therapeutic responses and hearing recovery.

α-Subunit Tyrosine Phosphorylation Is Required for Activation of the Large Conductance Ca2+-Activated Potassium Channel in the Rabbit Sphincter of Oddi.

Large conductance Ca2+-activated potassium (BKCa) channels are regulated by intracellular free Ca2+ concentrations ([Ca2+]i) and channel protein phosphorylation. In hypercholesterolemia (HC), motility impairment of the sphincter of Oddi (SO) is associated with abnormal [Ca2+]i accumulation in smooth muscle cells of the rabbit SO (RSOSMCs), which is closely related to BKCa channel activity. However, the underlying mechanisms regulating channel activity remain unclear. In this study, an HC rabbit model was generated and used to investigate BKCa channel activity of RSOSMCs via SO muscle tone measurement in vitro and manometry in vivo, electrophysiological recording, intracellular calcium measurement, and Western blot analyses. BKCa channel activity was decreased, which correlated with [Ca2+]i overload and reduced tyrosine phosphorylation of the BKCa α-subunit in the HC group. The abnormal [Ca2+]i accumulation and decreased BKCa channel activity were partially restored by Na3VO4 pretreatment but worsened by genistein in RSOSMCs in the HC group. This study suggests that α-subunit tyrosine phosphorylation is required for [Ca2+]i to activate BKCa channels, and there is a negative feedback between the BKCa channel and the L-type voltage-dependent Ca2+ channel that regulates [Ca2+]i. This study provides direct evidence that tyrosine phosphorylation of BKCa α-subunits is required for [Ca2+]i to activate BKCa channels in RSOSMCs, which may be the underlying physiological and pathologic mechanism regulating the activity of BKCa channels in SO cells.

Intersubject correlation analysis reveals the plasticity of cerebral functional connectivity in the long-term use of social media.

Owing to the limitations of cross-sectional studies, it is unclear whether social media induce brain changes, or if individuals with certain biological traits are more likely to use social media. Functional connectivity (FC) can reflect cerebral functional plasticity, and if social media can influence cerebral FC, then the FC of light social media users should be more similar to that of heavy users after they "heavily" used social media for a long period. We combined longitudinal study design and intersubject correlation (ISC) analysis to investigate this similarity. Thirty-five heavy and 21 light social media users underwent cognitive tests and functional MRIs. The 21 light social media users underwent another functional MRI scan after completing an additional four-week social media task. We conducted the ISC at the group, individual, and brain-region levels to investigate the similarity of FC and locate the brain regions most affected by social media. The FC of light social media users was more similar to that of heavy social media users after they completed the four-week social media task. Then, social media had an impact on half of the brain, involving almost all brain networks. Finally, cerebral FC that mostly affected by social media was associated with selective attention. We concluded that the impact of social media use on cerebral functional connectivity changes is revealed by ISC method and longitudinal design, which may provide guidance for clinical practice. The methods used in the current research could also be applied to similar domains.

Differentiating thymoma, thymic carcinoma and lymphoma based on collagen fibre patterns with T2- and diffusion-weighted magnetic resonance imaging.

OBJECTIVES: The amount and distribution of intratumoural collagen fibre vary among different thymic tumours, which can be clearly detected with T2- and diffusion-weighted MR images. To explore the incidences of collagen fibre patterns (CFPs) among thymomas, thymic carcinomas and lymphomas on imaging, and to evaluate the efficacy and reproducibility of CFPs in differential diagnosis of thymic tumours. MATERIALS AND METHODS: Three hundred and ninety-eight patients with pathologically diagnosed thymoma, thymic carcinoma and lymphoma who underwent T2- and diffusion-weighted MR imaging were retrospectively enrolled. CFPs were classified into four categories: septum sign, patchy pattern, mixed pattern and no septum sign. The incidences of CFPs were compared among different thymic tumours, and the efficacy and reproducibility in differentiating the defined tumour types were analysed. RESULTS: There were significant differences in CFPs among thymomas, thymic squamous cell carcinomas (TSCCs), other thymic carcinomas and neuroendocrine tumours (OTC&NTs) and thymic lymphomas. Septum signs were found in 209 (86%) thymomas, which differed between thymomas and any other thymic neoplasms (all p < 0.005). The patchy, mixed patterns and no septum sign were mainly seen in TSCCs (80.3%), OTC&NTs (78.9%) and thymic lymphomas (56.9%), respectively. The consistency of different CFP evaluation between two readers was either good or excellent. CFPs achieved high efficacy in identifying the thymic tumours. CONCLUSION: The CFPs based on T2- and diffusion-weighted MR imaging were of great value in the differential diagnosis of thymic tumours. KEY POINTS: • Significant differences are found in intratumoural collagen fibre patterns among thymomas, thymic squamous cell carcinomas, other thymic carcinomas and neuroendocrine tumours and thymic lymphomas. • The septum sign, patchy pattern, mixed pattern and no septum sign are mainly seen in thymomas (86%), thymic squamous cell carcinomas (80.3%), other thymic carcinomas and neuroendocrine tumours (79%) and thymic lymphomas (57%), respectively. • The collagen fibre patterns have high efficacy and reproducibility in differentiating thymomas, thymic squamous cell carcinomas and thymic lymphomas.

Predicting Subtype of Growth Hormone Pituitary Adenoma based on Magnetic Resonance Imaging Characteristics.

PURPOSE: This study aimed to investigate the value of magnetic resonance (MR) characteristics in differentiating the subtypes of growth hormone pituitary adenomas. MATERIALS AND METHODS: The clinical and MR imaging data of 70 patients with growth hormone pituitary adenoma confirmed by surgery and pathology were retrospectively analyzed. The tumors were divided into dense granular (DG; 36 cases) and sparse granular subtypes (SG; 34 cases). The tumors' MR features were analyzed, including the mean and maximum diameters, T2 signal intensity, T2 relative signal intensity (rSI), homogeneity, enhancement degree, and invasiveness (Knosp grade). Mann-Whitney U test and χ2 test were used to analyze MR characteristics between the 2 groups. The independent predictors and predictive probabilities of tumor subtypes were obtained via a logistic regression model, and the efficacy was compared by receiver operating characteristic curve. RESULTS: The mean and maximum diameters of growth hormone adenoma in DG and SG were 1.77 versus 2.45 and 1.95 versus 3.00 cm (median, P < 0.05), respectively. There was a significant difference between the 2 groups in T2 signal intensity and rSI (P values were 0.02 and 0.001, respectively). Most DG adenomas (86.1%) appeared as hypointense on T2 images, and 38.2% of SG adenomas were hyperintense. There was no significant difference in tumor homogeneity (P = 0.622). A significant difference was found in the Knosp grade between the 2 subtypes (P = 0.004). In addition, the enhancement degree of SG adenomas was significantly higher than that of DG adenomas (P = 0.001). Logistic regression analysis showed that high T2 rSI value and marked contrast enhancement were independent predictors of the 2 subtypes, and the odds ratios were 4.811 and 4.649, respectively. The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve, sensitivity, and specificity were 0.765, 0.882, and 0.500, respectively. CONCLUSIONS: There are significant differences in tumor size, T2 signal intensity, T2 rSI, enhancement degree, and invasiveness between DG and SG adenomas. The logistic model based on the marked contrast enhancement and high T2 rSI value has an important value in predicting the subtype of growth hormone adenoma.

Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T1-weighted Contrast-enhanced Imaging.

BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate the diagnostic performance of machine learning using radiomics modelfrom T1-weighted contrast enhanced imaging(T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM. METHODS: Seventy-sevenGBM patients, including 51 with true progression and 26 with pseudoprogression,who underwent standard treatment and T1CE, were retrospectively enrolled.Clinical information, including sex, age, KPS score, resection extent, neurological deficit and mean radiation dose, were also recorded collected for each patient. The whole tumor enhancementwas manually drawn on the T1CE image, and a total of texture 9675 features were extracted and fed to a two-step feature selection scheme. A random forest (RF) classifier was trained to separate the patients by their outcomes.The diagnostic efficacies of the radiomics modeland radiologist assessment were further compared by using theaccuracy (ACC), sensitivity and specificity. RESULTS: No clinical features showed statistically significant differences between true progression and pseudoprogression.The radiomic classifier demonstrated ACC, sensitivity, and specificity of 72.78%(95% confidence interval [CI]: 0.45,0.91), 78.36%(95%CI: 0.56,1.00) and 61.33%(95%CI: 0.20,0.82).The accuracy, sensitivity and specificity of three radiologists' assessment were66.23%(95% CI: 0.55,0.76), 61.50%(95% CI: 0.43,0.78) and 68.62%(95% CI: 0.55,0.80); 55.84%(95% CI: 0.45,0.66),69.25%(95% CI: 0.50,0.84) and 49.13%(95% CI: 0.36,0.62); 55.84%(95% CI: 0.45,0.66), 69.23%(95% CI: 0.50,0.84) and 47.06%(95% CI: 0.34,0.61), respectively. CONCLUSION: T1CE-based radiomics showed better classification performance compared with radiologists' assessment.The radiomics modelwas promising in differentiating pseudoprogression from true progression.

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.

BACKGROUND: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. METHODS: Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. RESULTS: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. CONCLUSIONS: Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Can the low and high b-value distribution influence the pseudodiffusion parameter derived from IVIM DWI in normal brain?

BACKGROUND: Our study aims to reveal whether the low b-values distribution, high b-values upper limit, and the number of excitation (NEX) influence the accuracy of the intravoxel incoherent motion (IVIM) parameter derived from multi-b-value diffusion-weighted imaging (DWI) in the brain. METHODS: This prospective study was approved by the local Ethics Committee and informed consent was obtained from each participant. The five consecutive multi-b DWI with different b-value protocols (0-3500 s/mm2) were performed in 22 male healthy volunteers on a 3.0-T MRI system. The IVIM parameters from normal white matter (WM) and gray matter (GM) including slow diffusion coefficient (D), fast perfusion coefficient (D*) and perfusion fraction (f) were compared for differences among defined groups with different IVIM protocols by one-way ANOVA. RESULTS: The D* and f value of WM or GM in groups with less low b-values distribution (less than or equal to 5 b-values) were significantly lower than ones in any other group with more low b-values distribution (all P <  0.05), but no significant differences among groups with more low b-values distribution (P > 0.05). In addition, no significant differences in the D, D* and f value of WM or GM were found between group with one and more NEX of low b-values distribution (all P > 0.05). IVIM parameters in normal WM and GM strongly depended on the choice of the high b-value upper limit. CONCLUSIONS: Metrics of IVIM parameters can be affected by low and high b value distribution. Eight low b-values distribution with high b-value upper limit of 800-1000 s/mm2 may be the relatively proper set when performing brain IVIM studies.

Neurovascular decoupling in type 2 diabetes mellitus without mild cognitive impairment: Potential biomarker for early cognitive impairment.

Type 2 diabetes mellitus (T2DM) is a significant risk factor for mild cognitive impairment (MCI) and the acceleration of MCI to dementia. The high glucose level induce disturbance of neurovascular (NV) coupling is suggested to be one potential mechanism, however, the neuroimaging evidence is still lacking. To assess the NV decoupling pattern in early diabetic status, 33 T2DM without MCI patients and 33 healthy control subjects were prospectively enrolled. Then, they underwent resting state functional MRI and arterial spin labeling imaging to explore the hub-based networks and to estimate the coupling of voxel-wise cerebral blood flow (CBF)-degree centrality (DC), CBF-mean amplitude of low-frequency fluctuation (mALFF) and CBF- mean regional homogeneity (mReHo). We further evaluated the relationship between NV coupling pattern and cognitive performance (false discovery rate corrected). T2DM without MCI patients displayed significant decrease in the absolute CBF-mALFF, CBF-mReHo coupling of CBFnetwork and in the CBF-DC coupling of DCnetwork. Besides, networks which involved CBF and DC hubs mainly located in the default mode network (DMN). Furthermore, less severe disease and better cognitive performance in T2DM patients were significantly correlated with higher coupling of CBF-DC, CBF-mALFF or CBF-mReHo, especially for the cognitive dimensions of general function and executive function. Thus, coupling of CBF-DC, CBF-mALFF and CBF-mReHo may serve as promising indicators to reflect NV coupling state and to explain the T2DM related early cognitive impairment.

Optimizing Texture Retrieving Model for Multimodal MR Image-Based Support Vector Machine for Classifying Glioma.

BACKGROUND: Accurate glioma grading plays an important role in patient treatment. PURPOSE: To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with support vector machine (SVM). STUDY TYPE: Retrospective. POPULATION: In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007. FIELD STRENGTH/SEQUENCE: 3.0T MRI/ T1 WI, T2 fluid-attenuated inversion recovery, contrast enhanced T1 , arterial spinal labeling, diffusion-weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm2 ), and dynamic contrast-enhanced. ASSESSMENT: Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size-zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF-SVM) combined with attribute selection using SVM-recursive feature elimination (SVM-RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10-fold crossvalidation. The model performance was further tested using the remaining 20% data. STATISTICAL TESTS: Ten-fold crossvalidation was used to validate the model performance. RESULTS: Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM-RFE was able to reduce attribute redundancy as well as improve RBF-SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray-level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM-RFE to provide comparable classifying efficacy. DATA CONCLUSION: When using image textures-based SVM classification of gliomas, the GLSZM model in combination with gray-level 64 and attribute selection may be an optimized solution. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1263-1274.

Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis.

BACKGROUND: The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients. METHODS: Ninety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher's exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables. RESULTS: MGMT promoter methylation was associated with tumor location and necrosis (P <  0.05). Significantly increased ADC value (P <  0.001) and decreased rCBF (P <  0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914. CONCLUSION: ADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.

Radiomics strategy for glioma grading using texture features from multiparametric MRI.

BACKGROUND: Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays. PURPOSE/HYPOTHESIS: To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps. STUDY TYPE: Retrospective; radiomics. POPULATION: A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively. FIELD STRENGTH/SEQUENCE: 3.0T MRI/T1 -weighted images before and after contrast-enhanced, T2 -weighted, multi-b-value diffusion-weighted and 3D arterial spin labeling images. ASSESSMENT: After multiparametric MRI preprocessing, high-throughput features were derived from patients' volumes of interests (VOIs). The support vector machine-based recursive feature elimination was adopted to find the optimal features for low-grade glioma (LGG) vs. high-grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency. STATISTICAL TESTS: Student's t-test or a chi-square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist. RESULTS: Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI. DATA CONCLUSION: Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision-making for patients with varied glioma grades. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518-1528.

Gender differences in functional connectivities between insular subdivisions and selective pain-related brain structures.

BACKGROUND: The incidence of pain disorders in women is higher than in men, making gender differences in pain a research focus. The human insular cortex is an important brain hub structure for pain processing and is divided into several subdivisions, serving different functions in pain perception. Here we aimed to examine the gender differences of the functional connectivities (FCs) between the twelve insular subdivisions and selected pain-related brain structures in healthy adults. METHODS: Twenty-six healthy males and 11 age-matched healthy females were recruited in this cross-sectional study. FCs between the 12 insular subdivisions (as 12 regions of interest (ROIs)) and the whole brain (ROI-whole brain level) or 64 selected pain-related brain regions (64 ROIs, ROI-ROI level) were measured between the males and females. RESULTS: Significant gender differences in the FCs of the insular subdivisions were revealed: (1) The FCs between the dorsal dysgranular insula (dId) and other brain regions were significantly increased in males using two different techniques (ROI-whole brain and ROI-ROI analyses); (2) Based on the ROI-whole brain analysis, the FC increases in 4 FC-pairs were observed in males, including the left dId - the right median cingulate and paracingulate/ right posterior cingulate gyrus/ right precuneus, the left dId - the right median cingulate and paracingulate, the left dId - the left angular as well as the left dId - the left middle frontal gyrus; (3) According to the ROI-ROI analysis, increased FC between the left dId and the right rostral anterior cingulate cortex was investigated in males. CONCLUSION: In summary, the gender differences in the FCs of the insular subdivisions with pain-related brain regions were revealed in the current study, offering neuroimaging evidence for gender differences in pain processing. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02820974 . Registered 28 June 2016.

Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial.

BACKGROUND: Standard therapy for Glioblastoma multiforme (GBM) involves maximal safe tumor resection followed with radiotherapy and concurrent adjuvant temozolomide. About 20 to 30% patients undergoing their first post-radiation MRI show increased contrast enhancement which eventually recovers without any new treatment. This phenomenon is referred to as pseudoprogression. Differentiating tumor progression from pseudoprogression is critical for determining tumor treatment, yet this capacity remains a challenge for conventional magnetic resonance imaging (MRI). Thus, a prospective diagnostic trial has been established that utilizes multimodal MRI techniques to detect tumor progression at its early stage. The purpose of this trial is to explore the potential role of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and three-dimensional arterial spin labeling imaging (3D-ASL) in differentiating true progression from pseudoprogression of GBM. In addition, the diagnostic performance of quantitative parameters obtained from IVIM-DWI and 3D-ASL, including apparent diffusion coefficient (ADC), slow diffusion coefficient (D), fast diffusion coefficient (D*), perfusion fraction (f), and cerebral blood flow (CBF), will be evaluated. METHODS: Patients that recently received a histopathological diagnosis of GBM at our hospital are eligible for enrollment. The patients selected will receive standard concurrent chemoradiotherapy and adjuvant temozolomide after surgery, and then will undergo conventional MRI, IVIM-DWI, 3D-ASL, and contrast-enhanced MRI. The quantitative parameters, ADC, D, D*, f, and CBF, will be estimated for newly developed enhanced lesions. Further comparisons will be made with unpaired t-tests to evaluate parameter performance in differentiating true progression from pseudoprogression, while receiver-operating characteristic (ROC) analyses will determine the optimal thresholds, as well as sensitivity and specificity. Finally, relationships between these parameters will be assessed with Pearson's correlation and partial correlation analyses. DISCUSSION: The results of this study may demonstrate the potential value of using multimodal MRI techniques to differentiate true progression from pseudoprogression in its early stages to help decision making in early intervention and improve the prognosis of GBM. TRIAL REGISTRATION: This study has been registered at ClinicalTrials.gov ( NCT02622620 ) on November 18, 2015 and published on March 28, 2016.

Multimodal MRI for early diabetic mild cognitive impairment: study protocol of a prospective diagnostic trial.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a risk factor for dementia. Mild cognitive impairment (MCI), an intermediary state between normal cognition and dementia, often occurs during the prodromal diabetic stage, making early diagnosis and intervention of MCI very important. Latest neuroimaging techniques revealed some underlying microstructure alterations for diabetic MCI, from certain aspects. But there still lacks an integrated multimodal MRI system to detect early neuroimaging changes in diabetic MCI patients. Thus, we intended to conduct a diagnostic trial using multimodal MRI techniques to detect early diabetic MCI that is determined by the Montreal Cognitive Assessment (MoCA). METHODS: In this study, healthy controls, prodromal diabetes and diabetes subjects (53 subjects/group) aged 40-60 years will be recruited from the physical examination center of Tangdu Hospital. The neuroimaging and psychometric measurements will be repeated at a 0.5 year-interval for 2.5 years' follow-up. The primary outcome measures are 1) Microstructural and functional alterations revealed with multimodal MRI scans including structure magnetic resonance imaging (sMRI), resting state functional magnetic resonance imaging (rs-fMRI), diffusion kurtosis imaging (DKI), and three-dimensional pseudo-continuous arterial spin labeling (3D-pCASL); 2) Cognition evaluation with MoCA. The second outcome measures are obesity, metabolic characteristics, lifestyle and quality of life. DISCUSSION: The study will provide evidence for the potential use of multimodal MRI techniques with psychometric evaluation in diagnosing MCI at prodromal diabetic stage so as to help decision making in early intervention and improve the prognosis of T2DM. TRIAL REGISTRATION: This study has been registered to ClinicalTrials.gov ( NCT02420470 ) on April 2, 2015 and published on July 29, 2015.

Functional-structural decoupling in visual network is associated with cognitive decline in patients with type 2 diabetes mellitus: evidence from a multimodal MRI analysis.

Type 2 diabetes mellitus (T2DM) and cognitive dysfunction are highly prevalent disorders worldwide. Although visual network (VN) alteration and functional-structural coupling are potential warning factors for mild cognitive impairment (MCI) in T2DM patients, the relationship between the three in T2DM without MCI is unclear. Thirty T2DM patients without MCI and twenty-nine healthy controls (HC) were prospectively enrolled. Visual components (VC) were estimated by independent component analysis (ICA). Degree centrality (DC), amplitude of low frequency fluctuation (ALFF) and fractional anisotropy (FA) were established to reflect functional and structural characteristics in these VCs respectively. Functional-structural coupling coefficients were further evaluated using combined FA and DC or ALFF. Partial correlations were performed among neuroimaging indicators and neuropsychological scores and clinical variables. Three VCs were selected using group ICA. Deteriorated DC, ALFF and DC-FA coefficients in the VC1 were observed in the T2DM group compared with the HC group, while FA and ALFF-FA coefficients in these three VCs showed no significant differences. In the T2DM group, DC in the VC1 positively correlated with 2 dimensions in the California Verbal Learning Test, including Trial 4 and Total trial 1-5. The impaired DC-FA coefficients in the VC1 markedly affected the Total perseverative responses % of the Wisconsin Card Sorting Test. These findings indicate that DC and DC-FA coefficients in VN may be potential imaging biomarkers revealing early cognitive deficits in T2DM.

Abnormalities of cerebral blood flow and the regional brain function in Parkinson's disease: a systematic review and multimodal neuroimaging meta-analysis.

Background: Parkinson's disease (PD) is a neurodegenerative disease with high incidence rate. Resting state functional magnetic resonance imaging (rs-fMRI), as a widely used method for studying neurodegenerative diseases, has not yet been combined with two important indicators, amplitude low-frequency fluctuation (ALFF) and cerebral blood flow (CBF), for standardized analysis of PD. Methods: In this study, we used seed-based d-mapping and permutation of subject images (SDM-PSI) software to investigate the changes in ALFF and CBF of PD patients. After obtaining the regions of PD with changes in ALFF or CBF, we conducted a multimodal analysis to identify brain regions where ALFF and CBF changed together or could not synchronize. Results: The final study included 31 eligible trials with 37 data sets. The main analysis results showed that the ALFF of the left striatum and left anterior thalamic projection decreased in PD patients, while the CBF of the right superior frontal gyrus decreased. However, the results of multimodal analysis suggested that there were no statistically significant brain regions. In addition, the decrease of ALFF in the left striatum and the decrease of CBF in the right superior frontal gyrus was correlated with the decrease in clinical cognitive scores. Conclusion: PD patients had a series of spontaneous brain activity abnormalities, mainly involving brain regions related to the striatum-thalamic-cortex circuit, and related to the clinical manifestations of PD. Among them, the left striatum and right superior frontal gyrus are more closely related to cognition. Systematic review registration: https://www.crd.york.ac.uk/ PROSPERO (CRD42023390914).

Commonalities and distinctions between the type 2 diabetes mellitus and Alzheimer's disease: a systematic review and multimodal neuroimaging meta-analysis.

Background: Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are aging related diseases with high incidence. Because of the correlation of incidence rate and some possible mechanisms of comorbidity, the two diseases have been studied in combination by many researchers, and even some scholars call AD type 3 diabetes. But the relationship between the two is still controversial. Methods: This study used seed-based d mapping software to conduct a meta-analysis of the whole brain resting state functional magnetic resonance imaging (rs-fMRI) study, exploring the differences in amplitude low-frequency fluctuation (ALFF) and cerebral blood flow (CBF) between patients (AD or T2DM) and healthy controls (HCs), and searching for neuroimaging evidence that can explain the relationship between the two diseases. Results: The final study included 22 datasets of ALFF and 22 datasets of CBF. The results of T2DM group showed that ALFF increased in both cerebellum and left inferior temporal gyrus regions, but decreased in left middle occipital gyrus, right inferior occipital gyrus, and left anterior central gyrus regions. In the T2DM group, CBF increased in the right supplementary motor area, while decreased in the middle occipital gyrus and inferior parietal gyrus. The results of the AD group showed that the ALFF increased in the right cerebellum, right hippocampus, and right striatum, while decreased in the precuneus gyrus and right superior temporal gyrus. In the AD group, CBF in the anterior precuneus gyrus and inferior parietal gyrus decreased. Multimodal analysis within a disease showed that ALFF and CBF both decreased in the occipital lobe of the T2DM group and in the precuneus and parietal lobe of the AD group. In addition, there was a common decrease of CBF in the right middle occipital gyrus in both groups. Conclusion: Based on neuroimaging evidence, we believe that T2DM and AD are two diseases with their respective characteristics of central nervous activity and cerebral perfusion. The changes in CBF between the two diseases partially overlap, which is consistent with their respective clinical characteristics and also indicates a close relationship between them. Systematic review registration: PROSPERO [CRD42022370014].

Gray Matter Volume Abnormality in Chronic Pain Patients With Depressive Symptoms: A Systemic Review and Meta-Analysis of Voxel-Based Morphometry Studies.

Background: Gray matter volume (GMV) alteration in specific brain regions has been widely regarded as one of the most important neuroplasticity features in chronic pain patients with depressive symptoms (CP-D). However, the consistent and significant results were still lacking. Thus, further exploration was suggested to be performed. Objectives: This study aimed to comprehensively collect the voxel-based morphometry (VBM) studies on GMV alteration between CP-D and healthy controls (HCs). And a systemic review and meta-analysis were made to explore the characteristic brain regions in chronic pain and depression comorbidity. Methods: Search of PubMed, MEDLINE, Web of Science, and Cochrane Library databases updated to July 13, 2021. The altered GMV between CP-D and HCs in VBM studies was included in this meta-analysis. In total, 18 studies (20 datasets) and 1320 participants (520 patients and 800 HCs) were included. The significant coordinate information (x, y, z) reported in standard space and the effect size (t-value or z-score) were extracted and analyzed by anisotropic effect size-signed differential mapping (AES-SDM) 5.15 software. Results: According to the main analysis results, CP-D showed significant and consistent increased GMV in the left hippocampus (HIP. L) and decreased GMV in the medial part of the left superior frontal gyrus (SFG. L, BA 10) compared to HCs. Subgroup analysis showed significant decreased GMV in the medial orbital part of SFG.R (BA 10) in neuropathic pain, as well as significant increased GMV in the right parahippocampal gyrus (PHG.R, BA 35), left hippocampus (HIP.L, BA 20), and right middle frontal gyrus (MFG.R) in musculoskeletal pain. Furthermore, meta-regression showed a positive relationship between the decreased GMV in the medial part of SFG.L and the percentage of female patients. Conclusion: GMV abnormality in specific brain areas (e.g., HIP.L and SFG) was robust and reproducible, which could be significantly involved in this comorbidity disease. The findings in this study may be a valuable reference for future research. Systematic Review Registration: [www.crd.york.ac.uk/prospero/].