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].

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.

Risk factor analysis for major mediastinal vessel invasion in thymic epithelial tumors based on multi-slice CT Imaging

Objective

To explore the characteristics and risk factors for major mediastinal vessel invasion in different risk grades of thymic epithelial tumors (TETs) based on computed tomography (CT) imaging, and to develop prediction models of major mediastinal artery and vein invasion.

Methods

One hundred and twenty-two TET patients confirmed by histopathological analysis who underwent thorax CT were enrolled in this study. Clinical and CT data were retrospectively reviewed for these patients. According to the abutment degree between the tumor and major mediastinal vessels, the arterial invasion was divided into grade I, II, and III (< 25%, 25 - 49%, and ≥ 50%, respectively); the venous invasion was divided into grade I and II (< 50% and ≥ 50%). The degree of vessel invasion was compared among different defined subtypes or stages of TETs using the chi-square tests. The risk factors associated with TET vascular invasion were identified using multivariate logistic regression analysis.

Results

Based on logistic regression analysis, male patients (β = 1.549; odds ratio, 4.824) and the pericardium or pleural invasion (β = 2.209; odds ratio, 9.110) were independent predictors of 25% artery invasion, and the midline location (β = 2.504; odds ratio, 12.234) and mediastinal lymphadenopathy (β = 2.490; odds ratio, 12.06) were independent predictors of 50% artery invasion. As for 50% venous invasion, the risk factors include midline location (β = 2.303; odds ratio, 10.0), maximum tumor diameter larger than 5.9 cm (β = 4.038; odds ratio, 56.736), and pericardial or pleural effusion (β = 1.460; odds ratio, 4.306). The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve (AUC), sensitivity, and specificity were 0.944, 84.6%, and 91.7% for predicting 50% artery invasion, and 0.913, 81.8%, and 86.0% for 50% venous invasion in TET patients, respectively.

Conclusion

Several CT features can be used as independent predictors of ≥50% artery or venous invasion. A multivariate logistic regression model based on CT features is helpful in predicting the vascular invasion grades in patients with TET.

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 (BK_Ca) 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 BK_Ca channel activity. However, the underlying mechanisms regulating channel activity remain unclear. In this study, an HC rabbit model was generated and used to investigate BK_Ca channel activity of RSOSMCs via SO muscle tone measurement in vitro and manometry in vivo, electrophysiological recording, intracellular calcium measurement, and Western blot analyses. BK_Ca channel activity was decreased, which correlated with [Ca²⁺]i overload and reduced tyrosine phosphorylation of the BK_Ca α-subunit in the HC group. The abnormal [Ca²⁺]i accumulation and decreased BK_Ca 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 BK_Ca channels, and there is a negative feedback between the BK_Ca channel and the L-type voltage-dependent Ca²⁺ channel that regulates [Ca²⁺]i. This study provides direct evidence that tyrosine phosphorylation of BK_Ca α-subunits is required for [Ca²⁺]i to activate BK_Ca channels in RSOSMCs, which may be the underlying physiological and pathologic mechanism regulating the activity of BK_Ca channels in SO cells.

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.

Changes of brain function in patients with type 2 diabetes mellitus measured by different analysis methods: A new coordinate-based meta-analysis of neuroimaging

Objective

Neuroimaging meta-analysis identified abnormal neural activity alterations in patients with type 2 diabetes mellitus (T2DM), but there was no consistency or heterogeneity analysis between different brain imaging processing strategies. The aim of this meta-analysis was to determine consistent changes of regional brain functions in T2DM via the indicators obtained by using different post-processing methods.

Methods

Since the indicators obtained using varied post-processing methods reflect different neurophysiological and pathological characteristics, we further conducted a coordinate-based meta-analysis (CBMA) of the two categories of neuroimaging literature, which were grouped according to similar data processing methods: one group included regional homogeneity (ReHo), independent component analysis (ICA), and degree centrality (DC) studies, while the other group summarized the literature on amplitude of low-frequency fluctuation (ALFF) and cerebral blood flow (CBF).

Results

The final meta-analysis included 23 eligible trials with 27 data sets. Compared with the healthy control group, when neuroimaging studies were combined with ReHo, ICA, and DC measurements, the brain activity of the right Rolandic operculum, right supramarginal gyrus, and right superior temporal gyrus in T2DM patients decreased significantly. When neuroimaging studies were combined with ALFF and CBF measurements, there was no clear evidence of differences in the brain function between T2DM and HCs.

Conclusion

T2DM patients have a series of spontaneous abnormal brain activities, mainly involving brain regions related to learning, memory, and emotion, which provide early biomarkers for clarifying the mechanism of cognitive impairment and neuropsychiatric disorders in diabetes.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=247071, PROSPERO [CRD42021247071].

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/].

Combining Dynamic Network Analysis and Cerebral Carryover Effect to Evaluate the Impacts of Reading Social Media Posts and Science Fiction in the Natural State on the Human Brain

Social media has been associated with decreased attention, memory, and learning abilities; however, the underlying mechanisms remain unclear. Dynamic function network connectivity (dFNC) analysis is suitable for uncovering dynamical brain activity. Besides, the effects of a cognitive task may persist for a while on the brain, even after the termination of the task, also known as the carryover effect. Consequently, we combined the dFNC analysis and cerebral carryover effects to study the brain dynamics of reading social media posts in the natural state and comparatively investigated the brain dynamics of reading science fiction on the smartphone. We performed functional MRI (fMRI) scans of all subjects at baseline and then assigned them a social media post or science fiction reading task. Immediately after, another fMRI scanning was performed for these subjects. We found that the change between dFNC states, the number of dFNC states, and the total distances increased after reading science fiction. Furthermore, the global, local, and nodal efficiencies of the deep-thinking state tended to increase after reading science fiction. On reading social media posts, the functional connectivity (FC) between the default mode network (DMN) and bilateral frontoparietal network (FPN) decreased, while the FC between DMN and visual network (VN) increased. Given the current evidence, we concluded that reading science fiction could substantially increase brain activity and network efficiency, while social media was related to abnormal FCs between DMN, VN, and FPN.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND

Large-scale functional connectivity (LSFC) patterns in the brain have unique intrinsic characteristics. Abnormal LSFC patterns have been found in patients with dementia, as well as in those with mild cognitive impairment (MCI), and these patterns predicted their cognitive performance. It has been reported that patients with type 2 diabetes mellitus (T2DM) may develop MCI that could progress to dementia. We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM, using connectome-based predictive modeling (CPM) and a support vector machine.

AIM

To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.

METHODS

Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Patients with T2DM were divided into two groups, according to the presence (T2DM-C; n = 16) or absence (T2DM-NC; n = 26) of MCI. Brain regions were marked using Harvard Oxford (HOA-112), automated anatomical labeling (AAL-116), and 264-region functional (Power-264) atlases. LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique. Subsequently, we used a support vector machine based on LSFC patterns for among-group differentiation. The area under the receiver operating characteristic curve determined the appearance of the classification.

RESULTS

CPM could predict the MoCA scores in patients with T2DM (Pearson’s correlation coefficient between predicted and actual MoCA scores, r = 0.32, P=0.0066 [HOA-112 atlas]; r = 0.32, P=0.0078 [AAL-116 atlas]; r = 0.42, P=0.0038 [Power-264 atlas]), indicating that LSFC patterns represent cognition-level measures in these patients. Positive (anti-correlated) LSFC networks based on the Power-264 atlas showed the best predictive performance; moreover, we observed new brain regions of interest associated with T2DM-related cognition. The area under the receiver operating characteristic curve values (T2DM-NC group vs. T2DM-C group) were 0.65-0.70, with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value (0.70). Most discriminative and attractive LSFCs were related to the default mode network, limbic system, and basal ganglia.

CONCLUSION

LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

Differentiation Between Primary Central Nervous System Lymphoma and Atypical Glioblastoma Based on MRI Morphological Feature and Signal Intensity Ratio: A Retrospective Multicenter Study

Objectives

To investigate the value of morphological feature and signal intensity ratio (SIR) derived from conventional magnetic resonance imaging (MRI) in distinguishing primary central nervous system lymphoma (PCNSL) from atypical glioblastoma (aGBM).

Methods

Pathology-confirmed PCNSLs (n = 93) or aGBMs (n = 48) from three institutions were retrospectively enrolled and divided into training cohort (n = 98) and test cohort (n = 43). Morphological features and SIRs were compared between PCNSL and aGBM. Using linear discriminant analysis, multiple models were constructed with SIRs and morphological features alone or jointly, and the diagnostic performances were evaluated via receiver operating characteristic (ROC) analysis. Areas under the curves (AUCs) and accuracies (ACCs) of the models were compared with the radiologists’ assessment.

Results

Incision sign, T₂ pseudonecrosis sign, reef sign and peritumoral leukomalacia sign were associated with PCNSL (training and overall cohorts, P < 0.05). Increased T₁ ratio, decreased T₂ ratio and T₂/T₁ ratio were predictive of PCNSL (all P < 0.05). ROC analysis showed that combination of morphological features and SIRs achieved the best diagnostic performance for differentiation of PCNSL and aGBM with AUC/ACC of 0.899/0.929 for the training cohort, AUC/ACC of 0.794/0.837 for the test cohort and AUC/ACC of 0.869/0.901 for the overall cohort, respectively. Based on the overall cohort, two radiologists could distinguish PCNSL from aGBM with AUC/ACC of 0.732/0.724 for radiologist A and AUC/ACC of 0.811/0.829 for radiologist B.

Conclusion

MRI morphological features can help differentiate PCNSL from aGBM. When combined with SIRs, the diagnostic performance was better than that of radiologists’ assessment.

Dynamic Alterations of Functional Connectivity and Amplitude of Low-Frequency Fluctuations in Patients with Unilateral Sudden Sensorineural Hearing Loss

Unilateral sudden sensorineural hearing loss (SSNHL) adversely affects the quality of life, leading to increased risk of depression and cognitive decline. Our previous studies have mainly focused on the static brain function abnormalities in SSNHL patients. However, the dynamic features of brain activity in SSNHL patients are not elucidated. To explore the dynamic brain functional alterations in SSNHL patients, age- and sex- matched SSNHL patients (n=38) and healthy controls (HC, n=44) were enrolled. The dynamic functional connectivity (dFC) and dynamic amplitude of low-frequency fluctuation (dALFF) methods were used to compare the temporal features and dynamic neural activity between the two groups. In dFC analyses, the multiple functional connectivities (FCs) were clustered into 2 different states; a greater proportion of FCs in SSNHL patients showed sparse state compared with HC. In dALFF analyses, SSNHL individuals exhibited decreased dALFF variability in bilateral inferior occipital gyrus, middle occipital gyrus, calcarine, right lingual gyrus, and right fusiform gyrus. dALFF variability showed a negative correlation with activated partial thromboplatin time. The dynamic characteristics of SSNHL patients were different from static functional connectivity and static amplitude of low-frequency fluctuation, especially within the visual cortices. These findings suggest that SSNHL patients experience cross-modal plasticity and visual compensation, which may be closely related to the pathophysiology of SSNHL.

Incidence of Cerebral Infarction in Northwest China From 2009 to 2018

BACKGROUND

There is a lack of epidemiological analysis of patients with cerebral infarction in northwest China. In the present investigation, we conducted a retrospective analysis to collect information on epidemiological characteristics of patients with cerebral infarction in five provinces of northwest China and the Shanxi Province of patients who were hospitalized in the Tangdu Hospital. This project should provide a scientific basis for active prevention and treatment of cerebral infarction.

MATERIAL AND METHODS

A retrospective analysis of patients with epidemic characteristics of cerebral infarction that were admitted to the Tangdu Hospital of northwest China from January 2009 to December 2018.

RESULTS

A total of 18,302 patients (aged 1-97 years) with confirmed cerebral infarction, including 12,201 males and 6,101 females, were retrospectively enrolled in this study. The most common lesion site was the cerebellum (51.5%). The incidence of cerebral infarction was slightly higher in workers and laborers, favoring male patients and those aged 40-70 years. The difference between men and women gradually increased after the age of 30.

CONCLUSIONS

In this study, 18,302 hospitalized patients with cerebral infarction from different occupations were included. Those engaged in physical labor were more likely to have a cerebral infarction. The incidence of cerebral infarction in males was higher than in females. Cerebellar and cerebral area infarctions were the most common.

Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes

Objective

To explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA).

Methods

Forty-nine patients with GH-secreting PA confirmed by the pathological analysis were included in this retrospective study. Texture parameters based on T1-, T2-, and contrast-enhanced T1-weighted images (T1C) were extracted and compared for differences between densely granulated (DG) and sparsely granulated (SG) somatotroph adenoma by using two segmentation methods [region of interest 1 (ROI₁), excluding the cystic/necrotic portion, and ROI₂, containing the whole tumor]. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy.

Results

Among 49 included patients, 24 were DG and 25 were SG adenomas. Nine optimal texture features with significant differences between two groups were obtained from ROI₁. Based on the ROC analyses, T1WI signatures from ROI₁ achieved the highest diagnostic efficacy with an AUC of 0.918, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 85.7, 72.0, 100.0, 100.0, and 77.4%, respectively, for differentiating DG from SG. Comparing with the T1WI signature, the T1C signature obtained relatively high efficacy with an AUC of 0.893. When combining the texture features of T1WI and T1C, the radiomics signature also had a good performance in differentiating the two groups with an AUC of 0.908. In addition, the performance got in all the signatures from ROI₂ was lower than those in the corresponding signature from ROI_1.

Conclusion

Texture signatures based on MR images may be useful biomarkers to differentiate subtypes of GH-secreting PA patients.

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.

Increased resting state functional irregularity of T2DM brains with high HbA1c: sign for impaired verbal memory function?

Glycosylated hemoglobin A1c (HbA1c) has been considered as a key contributor to impaired cognition in type 2 diabetes mellitus (T2DM) brains. However, how does it affect the brain and whether the glucose controlling can slow down the process are still unknown. In the current study, T2DM patients with high glycosylated hemoglobin level (HGL) and controls with normal glycosylated hemoglobin level (NGL) were enrolled to investigate the relationships between HbA1c, brain imaging characteristics and cognitive function. First, a series of cognitive tests including California Verbal Learning Test (CVLT) were conducted. Then, the functional irregularity based on resting state functional magnetic resonance imaging data was evaluated via a new data-driven brain entropy (BEN) mapping analysis method. We found that the HGLs exhibited significantly increased BEN in the right precentral gyrus (PreCG.R), the right middle frontal gyrus (MFG.R), the triangular and opercular parts of the right inferior frontal gyrus (IFGtriang.R and IFGoperc.R). The strengths of the functional connections of PreCG.R with the brainstem/cerebellum were decreased. Partial correlation analysis showed that HbA1c had a strong positive correlation to regional BEN and negatively correlated with some CVLT scores. Negative correlations also existed between the BEN of PreCG.R/IFGoperc.R and some CVLT scores, suggesting the correspondence between higher HbA1c, increased BEN and decreased verbal memory function. This study demonstrated the potential of BEN in exploring the functional alterations affected by HbA1c and interpreting the verbal memory function decline. It will help understanding the neurophysiological mechanism of T2DM-induced cognitive decline and taking effective prevention or treatment measures.

Classification of Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning From Disease-Related Images

Purpose

The diagnosis of prostate transition zone cancer (PTZC) remains a clinical challenge due to their similarity to benign prostatic hyperplasia (BPH) on MRI. The Deep Convolutional Neural Networks (DCNNs) showed high efficacy in diagnosing PTZC on medical imaging but was limited by the small data size. A transfer learning (TL) method was combined with deep learning to overcome this challenge.

Materials and methods

A retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (nine patients). Using T2-weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps, DCNN models were trained and compared between different TL databases (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning, or transductive transferring).

Results

PTZC and BPH can be classified through traditional DCNN. The efficacy of TL from natural images was limited but improved by transferring knowledge from the disease-related images. Furthermore, transductive TL from disease-related images had comparable efficacy to the fine-tuning method. Limitations include retrospective design and a relatively small sample size.

Conclusion

Deep TL from disease-related images is a powerful tool for an automated PTZC diagnostic system. In developing regions where only conventional MR scans are available, the accurate diagnosis of PTZC can be achieved via transductive deep TL from disease-related images.

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.

Distinguishing brain inflammation from grade II glioma in population without contrast enhancement: a radiomics analysis based on conventional MRI

PURPOSE

In populations without contrast enhancement, the imaging features of atypical brain parenchyma inflammations can mimic those of grade II gliomas. The aim of this study was to assess the value of the conventional MR-based radiomics signature in differentiating brain inflammation from grade II glioma.

METHODS

Fifty-seven patients (39 patients with grade II glioma and 18 patients with inflammation) were divided into primary (n = 44) and validation cohorts (n = 13). Radiomics features were extracted from T₁-weighted images (T₁WI) and T₂-weighted images (T₂WI). Two-sample t-test and least absolute shrinkage and selection operator (LASSO) regression were adopted to select features and build radiomics signature models for discriminating inflammation from glioma. The predictive performance of the models was evaluated via area under the receiver operating characteristic curve (AUC) and compared with the radiologists' assessments.

RESULTS

Based on the primary cohort, we developed T₁WI, T₂WI and combination (T₁WI + T₂WI) models for differentiating inflammation from glioma with 4, 8, and 5 radiomics features, respectively. Among these models, T₂WI and combination models achieved better diagnostic efficacy, with AUC of 0.980, 0.988 in primary cohort and that of 0.950, 0.925 in validation cohort, respectively. The AUCs of radiologist 1's and 2's assessments were 0.661 and 0.722, respectively.

CONCLUSION

The signature based on radiomics features helps to differentiate inflammation from grade II glioma and improved performance compared with experienced radiologists, which could potentially be useful in clinical practice.

MICA: A toolkit for multimodal image coupling analysis

BACKGROUND

Analytical methods of brain research involving across-voxel correlation between multimodal images are currently tedious and slow due to the amount of manual interaction required. We have developed a new software package to automate and simplify many of these tasks.

NEW METHOD AND RESULTS

Our software performs four primary functions to aid in research. First, it helps with consistent renaming of files preprocessed with other software, enabling more accurate analysis. Second, it automates ROI extraction using data from existing and custom brain atlases. Third, it performs coupling analysis to obtain across-voxel Pearson correlation coefficients between images of different modalities based on these brain atlases or custom ROIs. Fourth, it automatically performs multiple comparison correction to correct the P-value using two false discovery rate (FDR) methods and a Bonferroni method to reduce the false-positive rate.

COMPARISON WITH EXISTING METHODS

Previous researchers have investigated the couplings between blood supply and brain functional topology in healthy brains and those from patients with type 2 diabetes, chronic migraine, and schizophrenia. These studies conducted analyses of both the whole and parts of the brain in terms of neuronal activity and blood perfusion, but the procedures were laborious and time-consuming.

CONCLUSION

We have developed a convenient and time-saving software package using MATLAB 2014a to automate the data preparation and analysis of across-voxel coupling between multimodal images.

MR imaging of thymomas: a combined radiomics nomogram to predict histologic subtypes

OBJECTIVES

Accurately predicting the WHO classification of thymomas is urgently needed to optimize individualized therapeutic strategies. We aimed to develop and validate a combined radiomics nomogram for personalized prediction of histologic subtypes in patients with thymomas.

METHODS

A total of 182 thymoma patients were divided into training (n = 128) and test (n = 54) cohorts. Radiomics features were extracted from T2-weighted, T2-weighted fat suppression, and diffusion-weighted images to establish a radiomics signature in the training cohort. Multivariate logistic regression analysis was used to develop a combined radiomics nomogram that incorporated clinical, conventional MR imaging variables, apparent diffusion coefficient (ADC) value, and radiomics signature. The efficacy of clinical, conventional MR imaging, or ADC model was also evaluated respectively. The performances of different models were compared by receiver operating characteristic analysis and Delong test. The discrimination, calibration, and clinical usefulness of the combined radiomics nomogram were assessed.

RESULTS

The radiomics signature, consisting of 14 features, achieved favorable predictive efficacy in differentiating low-risk from high-risk thymomas, outperforming clinical, conventional MR imaging, and ADC models. The combined radiomics nomogram incorporating tumor shape, ADC value, and radiomics signature yielded the best performance (training cohort: area under the curve [AUC] = 0.946, test cohort: AUC = 0.878). The calibration curve and decision curve analysis indicated the clinical utility of the combined radiomics nomogram.

CONCLUSIONS

The radiomics signature is a useful tool that can be used to predict histologic subtypes of thymomas. The combined radiomics nomogram improved the individualized subtype prediction in patients with thymomas.

KEY POINTS

• Fourteen robust features were selected to develop a radiomics signature for preoperative prediction of thymoma subtype. • MRI-based radiomics signature can differentiate low-risk thymomas from high-risk thymomas with favorable predictive efficacy compared with clinical, conventional MR imaging, and ADC models. • Combined radiomics nomogram based on tumor shape, ADC value, and radiomics signature could improve the individualized subtype prediction in patients with thymomas.

Amide Proton Transfer Imaging in Predicting Isocitrate Dehydrogenase 1 Mutation Status of Grade II/III Gliomas Based on Support Vector Machine

Background

To compare the efficacies of univariate and radiomics analyses of amide proton transfer weighted (APT_W) imaging in predicting isocitrate dehydrogenase 1 (IDH1) mutation of grade II/III gliomas.

Methods

Fifty-nine grade II/III glioma patients with known IDH1 mutation status were prospectively included (IDH1 wild type, 16; IDH1 mutation, 43). A total of 1044 quantitative radiomics features were extracted from APT_W images. The efficacies of univariate and radiomics analyses in predicting IDH1 mutation were compared. Feature values were compared between two groups with independent t-test and receiver operating characteristic (ROC) analysis was applied to evaluate the predicting efficacy of each feature. Cases were randomly assigned to either the training (n = 49) or test cohort (n = 10) for the radiomics analysis. Support vector machine with recursive feature elimination (SVM-RFE) was adopted to select the optimal feature subset. The adverse impact of the imbalance dataset in the training cohort was solved by synthetic minority oversampling technique (SMOTE). Subsequently, the performance of SVM model was assessed on both training and test cohort.

Results

As for univariate analysis, 18 features were significantly different between IDH1 wild-type and mutant groups (P < 0.05). Among these parameters, High Gray Level Run Emphasis All Direction offset 8 SD achieved the biggest area under the curve (AUC) (0.769) with the accuracy of 0.799. As for radiomics analysis, SVM model was established using 19 features selected with SVM-RFE. The AUC and accuracy for IDH1 mutation on training set were 0.892 and 0.952, while on the testing set were 0.7 and 0.84, respectively.

Conclusion

Radiomics strategy based on APT image features is potentially useful for preoperative estimating IDH1 mutation status.

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.

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.

Incidence and radiological pattern of eosinophilic granuloma: a retrospective study in a Chinese tertiary hospital

Background

The incidence and radiological patterns of eosinophilic granuloma (EG) in China is not clear. We described the incidence, presentation, and imaging characteristics of Chinese EG patients in a tertiary hospital.

Methods

A retrospective chart review was performed from January 2004 to October 2017 at a single tertiary general hospital. Seventy-six patients were pathologically identified as EG. Besides, 60 patients with preoperative imaging diagnosis of “EG” were analyzed to reveal the radiological patterns and their diagnostic power.

Results

Fifty-three male and 23 female EG patients with a mean age of 18.1 ± 16.7 years (range 1–58 years) were retrospectively included. Significant differences were observed in gender (male to female = 2.3:1) and age (the highest incidence at the age of 0~5 years) for EG. EG predominantly involved the skeletal system: flat bones (31.43%) > irregular bones (24.76%) > long bones (22.86%) > other organs (20.95%). No obvious relationships between season, biochemical markers, and EG incidence were observed. The common presenting symptoms were pain followed with local mass, and most patients underwent surgical resection. Among 60 imagingly diagnosed “EG” patients from April 2009 to October 2017, only 22 were with histological confirmation. The correct diagnosis rates were 37.1% (13 out of 35), 16.7% (5 out of 30), and 22.2% (8 out of 36) for plain radiography, computed tomography (CT), and magnetic resonance imaging (MRI), respectively.

Conclusions

Chinese EG has a varied presentation, age distribution, and gender difference. EG diagnosis is still based on biopsy or histopathology instead of imaging techniques.

Electronic supplementary material

The online version of this article (10.1186/s13018-019-1158-1) contains supplementary material, which is available to authorized users.

Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

Purpose

This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients’ survival.

Patients and methods

A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients’ survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan–Meier curve was utilized to predict patients’ survival.

Results

Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of V_e were demonstrated to have higher accuracies (the accuracies for Extended Tofts_V_e^mean and Extended Tofts_V_e^median were 68.33% and 71.67%, respectively, while those for the Incremental_V_e^mean and Incremental_V_e^75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_V_e histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_K_trans^95th (AUC = 0.9265) and Extended Tofts_V_e^95th (AUC = 0.9154) performed better than their corresponding means (Patlak_K_trans^mean: AUC = 0.9118 and Extended Tofts_V_e^mean: AUC = 0.9044) in predicting patients’ overall survival (OS) at 18-month follow-up.

Conclusion

DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading.

Trial registration

NCT 02622620.

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.

Fetal brain development at 25-39 weeks gestational age: A preliminary study using intravoxel incoherent motion diffusion-weighted imaging

BACKGROUND

The fetal brain developmental changes of water diffusivity and perfusion has not been extensively explored.

PURPOSE/HYPOTHESIS

To evaluate the fetal brain developmental changes of water diffusivity and perfusion using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

STUDY TYPE

Prospective.

POPULATION

Seventy-nine normal singleton fetuses were scanned without sedation of healthy pregnant women.

FIELD STRENGTH/SEQUENCE

5 T MRI/T₁ /₂ -weighted image and IVIM-DWI.

ASSESSMENT

Pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) values were calculated in the frontal (FWM), temporal (TWM), parietal (PWM), and occipital white matter (OWM) as well as cerebellar hemisphere (CH), basal ganglia region (BGR), thalamus (TH), and pons using an IVIM model.

STATISTICAL TESTS

One-way analysis of variable (ANOVA) followed by Bonferroni post-hoc multiple comparison was employed to reveal the difference of IVIM parameters among the investigated brain regions. The linear and the nonlinear polynomial regression analyses were utilized to reveal the correlation between gestational age (GA) and IVIM parameters.

RESULTS

There were significant differences in both D (F(7,623) = 96.64, P = 0.000) and f values (F(7,623) = 2.361, P = 0.0219), but not D* values among the varied brain regions. D values from TWM (r²  = 0.1402, P = 0.0002), PWM (r²  = 0.2245, P = 0.0002), OWM (r²  = 0.2519, P = 0.0002), CH (r²  = 0.2245, P = 0.0002), BGR (r²  = 0.3393, P = 0.0001), TH (r²  = 0.1259, P = 0.0001), and D* value from pons (r²  = 0.2206, P = 0.0002) were significantly correlated with GA using linear regression analysis. Quadratic regression analysis led to results similar to those using the linear regression model.

DATA CONCLUSION

IVIM-DWI parameters may indicate fetal brain developmental alterations but the conclusion is far from reached due to the not as high-powered correlation between IVIM parameters and GA.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:899-909.

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.

Incidence and clinical variable inter-relationships of thymic epithelial tumors in northwest China

Background

Thymic epithelial tumors (TETs) are the most common primary thymus tumors, but neither the possible ethnical/regional differences in the incidence of TETs nor the inter-relationships among the clinical variables has been revealed in northwest China.

Methods

A retrospective chart review was performed among pathologically confirmed TET patients from January 2004 to December 2015 in a tertiary general hospital of northwest China and the incidence, clinical features and the inter-relationships among clinical variables were analyzed.

Results

A total of 603 pathologically confirmed TETs patients (age range, 5-78 years; 308 males) were enrolled and the most common lesion location was anterior mediastinum (98.5%), among them, 192 (31.8%) had myasthenia gravis (MG). Twenty-six (5.7%), 112 (24.6%), 83 (18.2%), 137 (30.1%), 74 (16.3%), and 23 (5.1%) patients fell into the World Health Organization (WHO) type A, AB, B1, B2, B3 and thymic carcinoma (TC), respectively. The incidence of TETs was slightly higher in the female population and the age group of 40-60 years old. In addition, MG predominantly coexisted with WHO types A-B3 TETs and the TETs with MG were smaller than those without MG. The correct diagnosis rates were 42.3% (77 out of 182), 61.1% (127 out of 208), 89.3% (250 out of 280) and 75.0% (3 out of 4) for chest X-ray, non-contrast computed tomography (CT), contrast CT scan and magnetic resonance imaging (MRI), respectively.

Conclusions

Distinct gender and age differences exist in the incidence of TETs and the A-B3 TETs are closely related with MG. Contrast CT scan plays more important role in diagnosing TETs.

Iodine Quantification Using Dual-Energy Computed Tomography for Differentiating Thymic Tumors

The aim of the study was to explore the efficacy of iodine quantification with dual-energy computed tomography (DECT) in differentiating thymoma, thymic carcinoma, and thymic lymphoma.

MATERIALS AND METHODS

Fifty-seven patients with pathologically confirmed low-risk thymoma (n = 16), high-risk thymoma (n = 15), thymic carcinoma (n = 14), and thymic lymphoma (n = 12) underwent chest contrast-enhanced DECT scan were enrolled in this study. Tumor DECT parameters including iodine-related Hounsfield unit (IHU), iodine concentration (IC), mixed HU (MHU), and iodine ratio in dual phase, slope of energy spectral HU curve (λ), and virtual noncontrast (VNC) were compared for differences among 4 groups by one-way analysis of variance. Receiver operating characteristic curve was used to determine the efficacy for differentiating the low-risk thymoma from other thymic tumor by defined parameters.

RESULTS

According to quantitative analysis, dual-phase IHU, IC, and MHU values in patients with low-risk thymoma were significantly increased compared with patients with high-risk thymoma, thymic carcinoma, and thymic lymphoma (P < 0.05/4).The venous phase IHU value yielded the highest performance with area under the curve of 0.893, 75.0% sensitivity, and 89.7% specificity for differentiating the low-risk thymomas from high-risk thymomas or thymic carcinoma at the cutoff value of 34.3 HU. When differentiating low-risk thymomas from thymic lymphoma, the venous phase IC value obtained the highest diagnostic efficacy with the area under the curve of 0.969, and sensitivity, specificity, and cutoff value were 87.5%, 100.0%, and 1.25 mg/mL, respectively.

CONCLUSIONS

Iodine quantification with DECT may be useful for differentiating the low-risk thymomas from other thymic tumors.

Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning

Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas.

Methods: One hundred and thirteen glioma patients were retrospectively included. Tumor images were segmented with a rectangular region of interest (ROI), which contained about 80% of the tumor. Then, 20% data were randomly selected and leaved out at patient-level as test dataset. AlexNet and GoogLeNet were both trained from scratch and fine-tuned from models that pre-trained on the large scale natural image database, ImageNet, to magnetic resonance images. The classification task was evaluated with five-fold cross-validation (CV) on patient-level split.

Results: The performance measures, including validation accuracy, test accuracy and test area under curve (AUC), averaged from five-fold CV of GoogLeNet which trained from scratch were 0.867, 0.909, and 0.939, respectively. With transfer learning and fine-tuning, better performances were obtained for both AlexNet and GoogLeNet, especially for AlexNet. Meanwhile, GoogLeNet performed better than AlexNet no matter trained from scratch or learned from pre-trained model.

Conclusion: In conclusion, we demonstrated that the application of CNN, especially trained with transfer learning and fine-tuning, to preoperative glioma grading improves the performance, compared with either the performance of traditional machine learning method based on hand-crafted features, or even the CNNs trained from scratch.

Structural and functional brain changes in perimenopausal women who are susceptible to migraine: a study protocol of multi-modal MRI trial

Background

As a common clinical symptom that often bothers midlife females, migraine is closely associated with perimenopause. Previous studies suggest that one of the most prominent triggers is the sudden decline of estrogen during perimenopausal period. Hormone replacement therapy (HRT) is widely used to prevent this suffering in perimenopausal women, but effective diagnostic system is lacked for quantifying the severity of the diseaase. To avoid the abuse and overuse of HRT, we propose to conduct a diagnostic trial using multimodal MRI techniques to quantify the severity of these perimenopausal migraineurs who are susceptible to the decline of estrogen.

Methods

Perimenopausal women suffering from migraine will be recruited from the pain clinic of our hospital. Perimenopausal women not suffering from any kind of headache will be recruited from the local community. Clinical assessment and multi-modal MR imaging examination will be conducted. A follow up will be conducted once half year within 3 years. Pain behavior, neuropsychology scores, fMRI analysis combined with suitable statistical software will be used to reveal the potential association between these above traits and the susceptibility of migraine.

Discussion

Multi-modal imaging features of both healthy controls and perimenopausal women who are susceptible to estrogen decline will be acquired. Imaging features will include volumetric characteristics, white matter integrity, functional characteristics, topological properties, and perfusion properties. Clinical information, such as basic information, blood estrogen level, information of migraine, and a bunch of neurological scale will also be used for statistic assessment. This clinical trial would help to build an effective screen system for quantifying the severity of illness of those susceptible women during the perimenopausal period.

Trial registration

This study has already been registered at Clinical Trials. gov (ID: NCT02820974). Registration date: September 28th, 2014.

Comparison between ultra-high and conventional mono b-value DWI for preoperative glioma grading

To compare the efficacy of ultra-high and conventional mono-b-value DWI for glioma grading, in 109 pathologically confirmed glioma patients, ultra-high apparent diffusion coefficient (ADC_uh)was calculated using a tri-exponential mode, distributed diffusion coefficients (DDCs) and α values were calculated using a stretched-exponential model, and conventional ADC values were calculated using a mono-exponential model. The efficacy and reliability of parameters for grading gliomas were investigated using receiver operating characteristic (ROC) curve and intra-class correlation (ICC) analyses, respectively. The ADC_uh values differed (P < 0.001) between low-grade gliomas (LGGs; 0.436 ×10⁻³ mm²/sec) and high-grade gliomas (HGGs; 0.285 × 10⁻³ mm²/sec). DDC, a and various conventional ADC values were smaller in HGGs (all P ≤ 0.001, vs. LGGs). The ADC_uh parameter achieved the highest diagnostic efficacy with an area under curve (AUC) of 0.993, 92.9% sensitivity and 98.8% specificity for glioma grading at a cutoff value of 0.362×10⁻³ mm²/sec. ADC_uh measurement appears to be an easy-to-perform technique with good reproducibility (ICC = 0.9391, P < 0.001). The ADC_uh value based in a tri-exponential model exhibited greater efficacy and reliability than other DWI parameters, making it a promising technique for glioma grading.

Intravoxel incoherent motion diffusion-weighted MR imaging parameters predict pathological classification in thymic epithelial tumors

We evaluated the performance of intravoxel incoherent motion (IVIM) parameters for preoperatively predicting the subtype and Masaoka stage of thymic epithelial tumors (TETs). Seventy-seven patients with pathologically confirmed TETs underwent a diffusion weighted imaging (DWI) sequence with 9 b values. Differences in the slow diffusion coefficient (D), fast perfusion coefficient (D), and perfusion fraction (f) IVIM parameters, as well as the multi b-value fitted apparent diffusion coefficient (ADC_mb), were compared among patients with low-risk (LRT) and high-risk thymomas (HRT) and thymic carcinomas (TC), and between early stage (stages I and II) and advanced stage (stages III and IV) TET patients. ADC_mb, D, and D values were higher in the LRT group than in the HRT or TC group, but did not differ between the HRT and TC groups. The mean ADC_mb, D, and D values were higher in the early stage TETs group than the advanced stage TETs group. The f values did not differ among the groups. These results suggest that IVIM DWI could be used to preoperatively predict subtype and Masaoka stage in TET 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.

Alterations of White Matter Integrity and Hippocampal Functional Connectivity in Type 2 Diabetes Without Mild Cognitive Impairment

Aims: To investigate the white matter (WM) integrity and hippocampal functional connectivity (FC) in type 2 diabetes mellitus (T2DM) patients without mild cognitive impairment (MCI) by using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively.

Methods: Twelve T2DM patients without MCI and 24 age, sex and education matched healthy controls (HC) were recruited. DTI and rs-fMRI data were subsequently acquired on a 3.0T MR scanner. Tract-based spatial statistics (TBSS) combining region of interests (ROIs) analysis was used to investigate the alterations of DTI metrics (fractional anisotropy (FA), mean diffusivity (MD), λ₁ and λ₂₃) and FC measurement was performed to calculate hippocampal FC with other brain regions. Cognitive function was evaluated by using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Brain volumes were also evaluated among these participants.

Results: There were no difference of MMSE and MoCA scores between two groups. Neither whole brain nor regional brain volume decrease was revealed in T2DM patients without MCI. DTI analysis revealed extensive WM disruptions, especially in the body of corpus callosum (CC). Significant decreases of hippocampal FC with certain brain structures were revealed, especially with the bilateral frontal cortex. Furthermore, the decreased FA in left posterior thalamic radiation (PTR) and increased MD in the splenium of CC were closely related with the decreased hippocampal FC to caudate nucleus and frontal cortex.

Conclusions: T2DM patients without MCI showed extensive WM disruptions and abnormal hippocampal FC. Moreover, the WM disruptions and abnormal hippocampal FC were closely associated.

Highlights-

T2DM patients without MCI demonstrated no obvious brain volume decrease.

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Extensive white matter disruptions, especially within the body of corpus callosum, were revealed with DTI analysis among the T2DM patients.

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Despite no MCI in T2DM patients, decreased functional connectivity between hippocampal region and some critical brain regions were detected.

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The alterations in hippocampal functional connectivity were closely associated with those of the white matter structures in T2DM patients.

This trial was registered to ClinicalTrials.gov (NCT02420470, https://www.clinicaltrials.gov/).

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.

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.

Resting-state functional magnetic resonance imaging shows altered brain network topology in Type 2 diabetic patients without cognitive impairment

We analyzed topology of brain functional networks in type 2 diabetes mellitus (T2DM) patients without mild cognitive impairment. We recruited T2DM patients without mild cognitive impairment (4 males and 8 females) and healthy control subjects (8 males and 16 females) to undergo cognitive testing and resting-state functional magnetic resonance imaging. Graph theoretical analysis of functional brain networks revealed abnormal small-world architecture in T2DM patients as compared to control subjects. The functional brain networks of T2DM patients showed increased path length, decreased global efficiency and disrupted long-distance connections. Moreover, reduced nodal characteristics were distributed in the frontal, parietal and temporal lobes, while increased nodal characteristics were distributed in the frontal, occipital lobes, and basal ganglia in the T2DM patients. The disrupted topological properties correlated with cognitive performance of T2DM patients. These findings demonstrate altered topological organization of functional brain networks in T2DM patients without mild cognitive impairment.

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.

The Close Exposure to Radiology Program: Educational Benefits to Medical Students

BACKGROUND

Radiology clerkships during medical school provide a suboptimal training experience in the Chinese medical doctor training program. Staff radiologists are heavily occupied with clinic tasks which decreases teaching quality. The close exposure to radiology program (CERP) is a novel pathway designed to improve teaching quality, yet students' expectations of the potential benefits of such a program and their willingness to join CERP still have not been investigated among Chinese medical students.

METHODS

A survey was conducted among medical students of both sexes with various majors and at different levels of training. The students were asked to identify the potential benefits of CERP as well as to indicate if they were willing to join CERP.

RESULTS

Of the 1,600 surveys distributed to medical students, 1,394 were returned and analyzed. Most of the returned surveys were from males (1,268, 91%), and most respondents had not had a radiology clerkship experience (1,376, 99%). Most responding students were in a 5-year training program (94%) and in their third grade of training (41%). More than 60% of the surveyed students acknowledged each of the 5 benefits listed on the survey, although no statistically significant differences were seen between sexes, training grades, those with and without prior radiology experience, program length, or majors in how the questions were answered. Students most willing to participate in CERP were those enrolled in a 5-year training program (71%) and those who had previous radiology clerkship experience (89%). Students least willing to join CERP were majoring in somatology medicine (54%) and medical psychology (55%), and only 45% of students in 8-year programs indicated a willingness to join CERP. Chi-square tests indicated that the willingness to join CERP was not associated with sex (χ²_{(df = 1393)} = 128.6, P=1.00), training program (χ²_{(df = 1393)} = 111.3, P=1.00), training grade (χ²_{(df = 1393)} = 266.1, P=1.00), major (χ²_{(df = 1393)} = 456.1, P=1.00), or previous experience with radiology (χ²_{(df = 1393)} = 142.2, P=1.00).

CONCLUSION

Medical students enrolled at Fourth Military Medical University developed an awareness of the potential benefits of CERP; however, this awareness did not correlate with their willingness to join CERP.

The imaging features of metanephric adenoma: a case report and review of literature

Metanephric adenoma (MA) is a rare epithelial tumor of the kidney with a characteristic histology. To date, the imaging features of the tumor have not been clearly described. Until now, MA was considered to be benign, but the majority of MA cases underwent nephrectomy. Here, we report a case of MA confirmed by surgical pathology, and we will analyze the ultrasound and computed tomography findings. The radiological features of MA are presented along with a brief review of the pertinent literature to deepen the understanding of MA’s imaging features.

Does dexmedetomidine as a neuraxial adjuvant facilitate better anesthesia and analgesia? A systematic review and meta-analysis

Background

Neuraxial application of dexmedetomidine (DEX) as adjuvant analgesic has been invetigated in some randomized controlled trials (RCTs) but not been approved because of the inconsistency of efficacy and safety in these RCTs. We performed this meta-analysis to access the efficacy and safety of neuraxial DEX as local anaesthetic (LA) adjuvant.

Methods

We searched PubMed, PsycINFO, Scopus, EMBASE, and CENTRAL databases from inception to June 2013 for RCTs that investigated the analgesia efficacy and safety for neuraxial application DEX as LA adjuvant. Effects were summarized using standardized mean differences (SMDs), weighed mean differences (WMDs) or odds ratio (OR) with suitable effect model. The primary outcomes were postoperative pain intensity and analgesic duration, bradycardia and hypotension.

Results

Sixteen RCTs involving 1092 participants were included. Neuraxial DEX significantly decreased postoperative pain intensity (SMD, −1.29; 95% confidence interval (CI), −1.70 to −0.89; P<0.00001), prolonged analgesic duration (WMD, 6.93 hours; 95% CI, 5.23 to 8.62; P<0.00001) and increased the risk of bradycardia (OR, 2.68; 95% CI, 1.18 to 6.10; P = 0.02). No evidence showed that neuraxial DEX increased the risk of other adverse events, such as hypotension (OR, 1.54; 95% CI, 0.83 to 2.85; P = 0.17). Additionally, neuraxial DEX was associated with beneficial alterations in postoperative sedation scores and number of analgesic requirements, sensory and motor block characteristics, and intro-operative hemodynamics.

Conclusion

Neuraxial DEX is a favorable LA adjuvant with better and longer analgesia. The greatest concern is bradycardia. Further large sample trials with strict design and focusing on long-term outcomes are needed.

Proliferative phenotype of pulmonary microvascular endothelial cells plays a critical role in the overexpression of CTGF in the bleomycin-injured rat

The pathogenesis of idiopathic pulmonary fibrosis (IPF) is not very clear, with evidence for the involvement of both inflammation and aberrant vascular remodeling (associated with angiogenesis). Pulmonary microvascular endothelial cells (PMVECs), which play a major role in inflammation, secrete cytokines that promote the transformation and collagen synthesis of fibroblasts. Moreover, angiogenesis is characterized by PMVEC proliferation. The main aim of this study was to confirm the role of PMVECs in pulmonary fibrosis. Accordingly, we observed the functional changes in PMVECs in bleomycin (BLM)-treated rats (pulmonary fibrosis model) in vivo, and compared them with those of rats with pneumonia. The proliferation phenotype and intracellular ionized calcium concentration ([Ca(2+)]i) of PMVECs from BLM-treated rats were also investigated. The functioning of PMVECs was abnormal in BLM-injured rats, particularly with regard to their proliferation and secretion of connective tissue growth factor (CTGF). [Ca(2+)]i was increased in the proliferated PMVECs from BLM-treated rats. The findings suggest that dysfunction of PMVECs characterized by overexpression of CTGF is critical in rat pulmonary injury induced by BLM, and is probably related with the proliferative phenotype and [Ca(2+)]i overload. It can be concluded from the results that proliferation of PMVECs plays an important role in the pathogenesis of BLM-induced PF.

Acute stress induces down-regulation of large-conductance Ca2+-activated potassium channels in the lateral amygdala

Large-conductance Ca2+-activated potassium channels (BKCa) are highly expressed in the lateral amygdala (LA), which is closely involved in assigning stress disorders, but data on their role in the neuronal circuits of stress disorders are limited. In the present study, a significant reduction in BKCa channel expression in the amygdala of mice accompanied anxiety-like behaviour induced by acute stress. Whole-cell patch-clamp recordings from LA neurons of the anxious animals revealed a pronounced reduction in the fast after-hyperpolarization (fAHP) of action potentials mediated by BKCa channels that led to hyperexcitability of the LA neurons. Activation of BKCa channels in the LA reversed stress-induced anxiety-like behaviour after stress. Furthermore, down-regulated BKCa channels notably increased the evoked NMDA receptor-mediated excitatory postsynaptic potentials at the thalamo-LA synapses. These data demonstrate, for the first time, that restraint stress-induced anxiety-like behaviour could at least partly be explained by alterations in the functional BKCa channels in the LA.