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Objective:To investigate the abnormalities of gray matter volume (GMV) and the synergistic changes in different cerebral regions in the first-episode and early-onset depression (EOD) patients.Methods:A total of 60 patients with untreated EOD (EOD group) and 64 healthy controls (control group) matched for age, gender, and education underwent high-resolution T 1WI MR scans. Voxel-based morphometry was used to calculate the cerebral GMV. The difference in GMV between the two groups was compared with the t-test. Different brain regions were selected as seeds for structural covariation network (SCN) analysis. Spearman correlation model was used to analyze the correlation between the GMV in different cerebral regions and illness duration as well as the scores of Hamilton rating scale for depression (HAMD) 17 items in EOD group. Results:Compared to control group, the EOD group had significantly increased GMV in the right orbitofrontal cortex, right dorsolateral prefrontal cortex, right inferior parietal lobule, right superior parietal lobule and bilateral precuneus ( P<0.05, corrected by FDR). Based on the right orbitofrontal cortex and dorsolateral prefrontal cortex as seed regions, structural covariance analysis revealed that abnormal cooperative brain regions in EOD group, mainly distributed in the bilateral frontal lobe, parietal lobe, occipital lobe, temporal lobe, paralimbic system and cerebellum ( P<0.05, corrected by FDR). In EOD group, significant negative correlations were observed between the GMV in the right orbitofrontal cortex ( r=-0.314, P=0.015), the left precuneus ( r=-0.283, P=0.029), and illness duration. Significant positive correlations were observed between the GMV in the right dorsolateral prefrontal cortex and the scores of anxiety/somatization factor of HAMD17 ( r=0.331, P=0.010), the left precuneus and weight factor of HAMD17 ( r=0.255, P=0.049), respectively. Conclusions:Abnormal GMV changes are observed in some regions of the prefrontal and parietal lobule in patients with untreated EOD, accompanied by extensive covariant brain regions and additional structural connectivity. In addition, the abnormal GMV changes in some regions are associated with clinical features. Part of the prefrontal and parietal lobule may be the biomarkers to objectively evaluate abnormal brain structure in depression patients in the early stage.
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Objective:To investigate the abnormal dynamic characteristics of brain functional connectivity in patients with neuropsychiatric systemic lupus erythematosus (NPSLE) and its correlation with clinical indicators by using dynamic functional connectivity (dFC) analysis based on independent component analysis (ICA).Methods:The clinical and imaging data of female NPSLE patients diagnosed in the First Affiliated Hospital of Zhengzhou University from July 2018 to September 2019 were prospectively collected. The levels of complement C3, C4, CH50, glucocorticoid prednisone dosage, systemic lupus erythematosus disease activity index (SLEDAI) score and Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology (ACR) damage index (SDI) score were recorded. Age and sex matched healthy controls (HC) were enrolled at the same time. All subjects underwent resting state functional MRI (rs-fMRI). The spatial group independent component analysis was performed on MRI data using GIFT software, and 29 independent components (IC) were selected as internal connectivity network; five functional connectivity states and three dFC indexes (fraction time, mean dwell time, number of transitions) were obtained by using sliding time window technology. Two independent sample t test was used to calculate the difference of functional connectivity in different states. Mann-Whitney U test was used to calculate the difference of dFC indexes between groups. Spearman correlation analysis was used to calculate the correlation between dFC index and clinical data in NPSLE group. Results:A total of 45 NPSLE patients and 35 HC patients were enrolled. There was no significant difference in age and education level between the two groups ( t=-0.327, -0.460, P>0.05). Compared with HC group, NPSLE group had higher fraction time and longer mean dwell time ( Z=-2.496, -2.462, P<0.05); in state3 strong connection, compared with HC group, functional connectivity (FC) between posterior cerebellar lobe (IC39) and basal ganglia (IC10) was enhanced ( t=-5.201, P<0.05); FC was found decreased between posterior cerebellar lobe (IC39) and temporal lobe (IC5), temporal lobe (IC7), superior parietal lobe (IC65) ( t=4.212, 5.572, 4.415, P<0.05), as well as between paracentral lobular region (IC12) and posterior cingulate gyrus (IC15) ( t=3.893, P<0.05) in NPSLE group. The SDI score of NPSLE patients was negatively correlated with the fraction time and mean dwell time of state1 and state3 strong connection state ( P<0.05), and the SLEDAI score was negatively correlated with the fraction time and mean dwell time of state1 and state2 ( P<0.05). The SDI and SLEDAI scores were positively correlated with the fraction time and mean dwell time of state4 weak connection state, respectively ( P<0.05). The levels of serum complement C3, C4 and CH50 in NPSLE patients were positively correlated with the number of transitions ( r=0.428, 0.354, 0.385, P<0.05), and the dosage of prednisone was negatively correlated with the number of transitions ( r=-0.466, P<0.05). The validation analysis results showed the experimental results could be effectively repeated. Conclusion:The dFC analysis method based on ICA can effectively identify the alterations of brain functional connectivity on a shorter time scale, which may provide a new perspective for further exploration of the neuroimaging mechanism of cognitive impairment in NPSLE.
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Objective To investigate the difference of the motif patterns in brain directed functional network between first-episode schizophrenia patients and healthy controls, and to analysis the alterations of the underlying information flow patterns in patient networks. Methods The resting-state functional MRI data were collected from 44 first-episode schizophrenia patients and 39 healthy controls. The convergent cross mapping approach was employed to measure the causality connections between brain regions, and the directed functional networks were constructed. The calculations of the frequency and probability spectrum of all motif classes were performed at both whole brain and modular connected level. The between-group difference was then calculated. Results Compared with healthy controls, the frequency spectrum values of all motif classes in schizophrenia were significantly reduced (P＜0.05, FDR corrected), the Z scores of frequency spectrum of were decreased in chain-like motifs and increased in loop-like motifs. In the two groups, the probability values were higher at modular level than at whole brain level in two loop-like motifs (P＜0.05). Conclusion The present study revealed a loss in brain directed functional connections and abnormal alterations in the basic information flow patterns in first-episode schizophrenia brain.
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Objective To investigate the value of ADC and T2WI in differentiating of skull base chordoma and invasive pituitary adenomas(IPA).Methods 15 patients with skull base chordoma and 19 patients with IPA which involve paranasal sinus were reviewed retrospectively.All diagnosis were demonstrated by pathology.Quantitative analysis of minimum ADC, normal ADC and rT2WI values were performed.Differences in minimum ADC, normal ADC and rT2WI values between skull base chordoma and IPA were evaluated using the independent samples t test and receiver operating curves(ROC).Results Statistical analysis revealed a significant difference among normal ADC, minimum ADC and rT2WI values (P<0.01),and the area under the ROC curves decreased in turn.Conclusion Both ADC values and rT2WI SI are effective parameter for differentiating diagnosis of skull base chordoma and IPA.
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Objective To assess the value of diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) in differential diagnosis of neuroendocrine carcinoma of the uterine cervix (NECUC) from other tumors.Methods A total of 12 NECUCs,39 cervical squamous cell carcinomas (SCC) and 21 cervical adenocarcinomas (CA) confirmed by pathology were analyzed retrospectively.All the patients underwent conventional MRI and DWI.The ADC values were measured and compared among NECUC,SCC and CA.Diagnostic performance of ADC was compared using receiver operating characteristic curves (ROC).Results The mean ADC values of NECUC,SCC and CA were (0.66 ± 0.11) ×10-3 mm2/s,(0.86 ± 0.11) × 10-3 mm2/s and (1.04 ± 0.17) × 10-3 mm2 / s,with statistical differences between any two groups (P ＜0.001).The optimal cutoff values of ADC for differentiating NECUC and SCC was 0.681 ×10-3mm2/s with a sensitivity of 94.9％,specificity of 75.0％ and accuracy of 90.2％.The optimal cutoff values of ADC for differentiating NECUC and CA was 0.824× 10-3mm2/s with a sensitivity of 95.2％,specificity of 91.7％ and accuracy of 98.9％.Conclusion The differences of the mean ADC value are helpful for the differential diagnosis of NECUC,SCC and CA.
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Objective To investigate the diagnostic value of ADC in the evaluation of small round cell malignant tumors(SRCMT) of nasal and paranasal sinus.Methods This study included 143 patients with surgically confirmed SRCMT and Non-SRCMT of nasal and paranasal sinus between 2008 and 2015, all patients underwent diffusion weighted MRI at 3.0 T with a b factor of 0 and 1 000 s/mm2.Quantitative analysis of ADC values was performed.Difference in ADC values between SRCMT and Non-SRCMT was evaluated using the independent samples t test.One-way analysis of variance(ANOVA) test was performed to compare the ADC values of SRCMT.Receiver operating curves (ROC) were developed to determine the cutoff points to differentiate SRCMT from Non-SRCMT.Results There were 98 SRCMT, of which 20 lesions were rhabdomyosarcoma(RMS), 19 lesions were non-Hodgkin's lymphoma(NHL), 4 lesions were malignant melanoma(MM), 14 lesions were neuroendocrine carcinoma(NEC), 12 lesions were Ewing sarcoma or primitive neuroectodermal tumor(EWS or PNET), 11 lesions were extramedullary plasmacytoma(EMP), and 8 lesions were olfactory neuroblastoma(ON).There were 45 Non-SRCMT, of which 28 lesions were squamous cell carcinoma(SCC) and 17 lesions were adenoid cystic carcinoma(ACC).The mean ADC value of SRCMT[(0.66 ± 0.12) × 10-3mm2/s] was significantly different (t=14.97, P＜0.01) from Non-SRCMT [(1.02± 0.16)× 10-3mm2/s].All of 7 kinds of SRCMT were divided into 3 groups according to ADC values: NHL,MM, NEC,EMP;RMS,EWS,PNET;ON.There was statistically significant difference among all 3 groups(F=39.743, P＜0.01), and the differences between any 2 groups were still statistically significant.The area under the ROC of ADC values diagnosing SRCMT was 0.975.Compared with pathological results, an ADC value of 0.82 × 10-3mm2/s was used as the threshold for diagnosing SRCMT with a sensitivity of 97.8％ (44/45),specificity of 89.8％(88/98), and accuracy of 92.3％ (132/143).ADC value had high correlations compared with pathological results (Kappa value was 0.831).Conclusion The ADC value is a non-invasive imaging parameter that can be used to effectively assess SRCMT of nasal and paranasal sinus.