Abnormal Brain State in Major Depressive Disorder: A Resting-State Magnetic Resonance Study

Background: Respective changes in resting-state linear and nonlinear measures in major depressive disorder (MDD) have been reported. However, few studies have used integrated measures of linear and nonlinear brain dynamics to explore the pathological mechanisms underlying MDD. Method: Forty-two patients with MDD and 42 sex- and age-matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging to calculate multiscale entropy (MSE) and regional homogeneity (ReHo). The MSE-ReHo coupling of the whole gray matter and the MSE/ReHo ratio (the complexity of intensity homogeneity per unit time series) of each voxel were compared between the two groups. To evaluate the discriminative capacity of ratio features between patients with MDD and HC, we employed the support vector machine (SVM) learning method. Results: We observed that patients with MDD displayed increased MSE/ReHo ratio mainly in the orbitofrontal cortex, sensorimotor areas, and visual cortex. Moreover, significant correlations were observed between MSE/ReHo ratio and clinical indicators, including depression severity and cognitive function tests. The SVM model demonstrated high accuracy in differentiating patients with MDD from HC, highlighting the potential of the MSE/ReHo ratio as a diagnostic and prognostic tool. Conclusions: The aberrant MSE/ReHo ratio implicated the underlying mechanisms of depressive symptoms and cognitive impairment in patients with MDD. It may represent a critical state of the brain region, reflecting the degree of chaos and order in the brain region. Integrating linear and nonlinear combinations of brain signals holds promise for diagnosing psychiatric disorders.

Radiofrequency coil design for improving human liver fat quantification in a portable single-side magnetic resonance system

Earlier diagnosis of nonalcoholic fatty liver disease (NAFLD) is important to prevent progression of the disease. Recently, a low-cost portable magnetic resonance (MR) system was developed as a point-of-care screening tool for in vivo liver fat quantification. However, subcutaneous fat may confound the liver fat quantification, particularly in the NAFLD population. In this work, we propose a novel radiofrequency (RF) coil design composed of a set of "saturation" coils sandwiching a main coil to improve human liver fat quantification. By comparison with conventional MR imaging, we demonstrate the capability and effectiveness of the novel RF coil design in phantom experiments as well as in vivo liver scans. In the phantom experiment, the saturation coil reduced the error in the measured proton density fat fraction (PDFF) results from 28.9% to 4.0%, and in the in vivo experiment, it reduced the discrepancy in the PDFF results from 13.2% to 4.0%. The novel coil design, together with the adapted Carr-Purcell-Meiboom-Gill-based sequence, improves the practicability and robustness of the portable single-side MR system.

Monitoring the level of hypnosis using a hierarchical SVM system

Monitoring level of hypnosis is a major ongoing challenge for anesthetists to reduce anesthetic drug consumption, avoiding intraoperative awareness and prolonged recovery. This paper proposes a novel automated method for accurate assessing of the level of hypnosis with sevoflurane in 17 patients using the electroencephalogram signal. In this method, a set of distinctive features and a hierarchical classification structure based on support vector machine (SVM) methods, is proposed to discriminate the four levels of anesthesia (awake, light, general and deep states). The first stage of the hierarchical SVM structure identifies the awake state by extracting Shannon Permutation Entropy, Detrended Fluctuation Analysis and frequency features. Then deep state is identified by extracting the sample entropy feature; and finally light and general states are identified by extracting the three mentioned features of the first step. The accuracy of the proposed method of analyzing the brain activity during anesthesia is 94.11%; which was better than previous studies and also a commercial monitoring system (Response Entropy Index).

Gene selection of rat hepatocyte proliferation using adaptive sparse group lasso with weighted gene co-expression network analysis

Grouped gene selection is the most important task for analyzing the microarray data of rat liver regeneration. Many existing gene selection methods cannot outstand the interactions among the selected genes. In the process of rat liver regeneration, one of the most important events involved in many biological processes is the proliferation of rat hepatocytes, so it can be used as a measure of the effectiveness of the method. Here we proposed an adaptive sparse group lasso to select genes in groups for rat hepatocyte proliferation. The weighted gene co-expression networks analysis was used to identify modules corresponding to gene pathways, based on which a strategy of dividing genes into groups was proposed. A strategy of adaptive gene selection was also presented by assessing the gene significance and introducing the adaptive lasso penalty. Moreover, an improved blockwise descent algorithm was proposed. Experimental results demonstrated that the proposed method can improve the classification accuracy, and select less number of significant genes which act jointly in groups and have direct or indirect effects on rat hepatocyte proliferation. The effectiveness of the method was verified by the method of rat hepatocyte proliferation.

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders

PURPOSE

The purpose of this study is to introduce and evaluate the mixed structure regularization (MSR) approach for a deep sparse autoencoder aimed at unsupervised abnormality detection in medical images. Unsupervised abnormality detection based on identifying outliers using deep sparse autoencoders is a very appealing approach for computer-aided detection systems as it requires only healthy data for training rather than expert annotated abnormality. However, regularization is required to avoid overfitting of the network to the training data.

METHODS

We used coronary computed tomography angiography (CCTA) datasets of 90 subjects with expert annotated centerlines. We segmented coronary lumen and wall using an automatic algorithm with manual corrections where required. We defined normal coronary cross section as cross sections with a ratio between lumen and wall areas larger than 0.8. We divided the datasets into training, validation, and testing groups in a tenfold cross-validation scheme. We trained a deep sparse overcomplete autoencoder model for normality modeling with random structure and noise augmentation. We assessed the performance of our deep sparse autoencoder with MSR without denoising (SAE-MSR) and with denoising (SDAE-MSR) in comparison to deep sparse autoencoder (SAE), and deep sparse denoising autoencoder (SDAE) models in the task of detecting coronary artery disease from CCTA data on the test group.

RESULTS

The SDAE-MSR achieved the best aggregated area under the curve (AUC) with a 20% improvement and the best aggregated Average Precision (AP) with a 30% improvement upon the SAE and SDAE (AUC: 0.78 to 0.94, AP: 0.66 to 0.86) in distinguishing between coronary cross sections with mild stenosis (stenosis grade < 0.3) and coronary cross sections with severe stenosis (stenosis grade > 0.7). The improvements were statistically significant (Mann-Whitney U-test, P < 0.001). Similarly, The SDAE-MSR achieved the best aggregated AUC (AP) with an 18% (18%) improvement upon the SAE and SDAE (AUC: 0.71 to 0.84, AP: 0.68 to 0.80). The improvements were statistically significant (Mann-Whitney U-test, P < 0.05).

CONCLUSION

Deep sparse autoencoders with MSR in addition to explicit sparsity regularization term and stochastic corruption of the input data with Gaussian noise have the potential to improve unsupervised abnormality detection using deep-learning compared to common deep autoencoders.

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines

BACKGROUND

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images.

METHODS

T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy).

RESULTS

Eight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively.

CONCLUSION

The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

Stratification of patients with liver fibrosis using dual-energy CT

Assessing the severity of liver fibrosis has direct clinical implications for patient diagnosis and treatment. Liver biopsy, typically considered the gold standard, has limited clinical utility due to its invasiveness. Therefore, several imaging-based techniques for staging liver fibrosis have emerged, such as magnetic resonance elastography (MRE) and ultrasound elastography (USE), but they face challenges that include limited availability, high cost, poor patient compliance, low repeatability, and inaccuracy. Computed tomography (CT) can address many of these limitations, but is still hampered by inaccuracy in the presence of confounding factors, such as liver fat. Dual-energy CT (DECT), with its ability to discriminate between different tissue types, may offer a viable alternative to these methods. By combining the "multi-material decomposition" (MMD) algorithm with a biologically driven hypothesis we developed a method for assessing liver fibrosis from DECT images. On a twelve-patient cohort the method produced quantitative maps showing the spatial distribution of liver fibrosis, as well as a fibrosis score for each patient with statistically significant correlation with the severity of fibrosis across a wide range of disease severities. A preliminary comparison of the proposed algorithm against MRE showed good agreement between the two methods. Finally, the application of the algorithm to longitudinal DECT scans of the cohort produced highly repeatable results. We conclude that our algorithm can successfully stratify patients with liver fibrosis and can serve to supplement and augment current clinical practice and the role of DECT imaging in staging liver fibrosis.

Evolution-based hierarchical feature fusion for ultrasonic liver tissue characterization

This paper presents an evolution-based hierarchical feature fusion system that selects the dominant features among multiple feature vectors for ultrasonic liver tissue characterization. After extracting the spatial gray-level dependence matrices, multiresolution fractal feature vectors and multiresolution energy feature vectors, the system utilizes evolution-based algorithms to select features. In each feature space, features are selected independently to compile a feature subset. As the features of different feature vectors contain complementary information, a feature fusion process is used to combine the subsets generated from different vectors. Features are then selected from the fused feature vector to form a fused feature subset. The selected features are used to classify ultrasonic images of liver tissue into three classes: hepatoma, cirrhosis, and normal liver. Experiment results show that the classification accuracy of the fused feature subset is superior to that derived by using individual feature subsets. Moreover, the findings demonstrate that the proposed algorithm is capable of selecting discriminative features among multiple feature vectors to facilitate the early detection of hepatoma and cirrhosis via ultrasonic liver imaging.

Monitoring brain tumor vascular heamodynamic following anti-angiogenic therapy with advanced magnetic resonance imaging in mice

Advanced MR imaging methods have an essential role in classification, grading, follow-up and therapeutic management in patients with brain tumors. With the introduction of new therapeutic options, the challenge for better tissue characterization and diagnosis increase, calling for new reliable non-invasive imaging methods. In the current study we evaluated the added value of a combined protocol of blood oxygen level dependent (BOLD) imaging during hyperoxic challenge (termed hemodynamic response imaging (HRI)) in an orthotopic mouse model for glioblastoma under anti-angiogenic treatment with B20-4.1.1, an anti-VEGF antibody. In glioblastoma tumors, the elevated HRI indicated progressive angiogenesis as further confirmed by histology. In the current glioblastoma model, B20-treatment caused delayed tumor progression with no significant changes in HRI yet with slightly reduced tumor vascularity as indicated by histology. Furthermore, fewer apoptotic cells and higher proliferation index were detected in the B20-treated tumors compared to control-treated tumors. In conclusion, HRI provides an easy, safe and contrast agent free method for the assessment of the brain hemodynamic function, an additionally important clinical information.

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods

BACKGROUND AND OBJECTIVE

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment.

METHODS

From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models.

RESULTS

The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively.

CONCLUSIONS

The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment.

Quantitative functional MRI biomarkers improved early detection of colorectal liver metastases

PURPOSE

To implement and evaluate the performance of a computerized statistical tool designed for robust and quantitative analysis of hemodynamic response imaging (HRI) -derived maps for the early identification of colorectal liver metastases (CRLM).

MATERIALS AND METHODS

CRLM-bearing mice were scanned during the early stage of tumor growth and subsequently during the advanced-stage. Three experienced radiologists marked various suspected-foci on the early stage anatomical images and classified each as either highly certain or as suspected tumors. The statistical model construction was based on HRI maps (functional-MRI combined with hypercapnia and hyperoxia) using a supervised learning paradigm which was further trained either with the advanced-stage sets (late training; LT) or with the early stage sets (early training; ET). For each group of foci, the classifier results were compared with the ground-truth.

RESULTS

The ET-based classification significantly improved the manual classification of the highly certain foci (P < 0.05) and was superior compared with the LT-based classification (P < 0.05). Additionally, the ET-based classification, offered high sensitivity (57-63%), accompanied with high positive predictive value (>94%) and high specificity (>98%) for suspected-foci.

CONCLUSION

The ET-based classifier can strengthen the radiologist's classification of highly certain foci. Additionally, it can aid in classifying suspected-foci, thus enabling earlier intervention which can often be lifesaving.

Quantifying hepatic fibrosis using a biexponential model of diffusion weighted imaging in ex vivo liver specimens

The purpose of this study was to evaluate the non-Gaussian behavior of diffusion related signal decay of the ex vivo murine liver tissues from a dietary model of hepatic fibrosis. To this end, a biexponential formalism was used to model high b-value diffusion imaging (up to 3500 s/mm(2)), the findings of which were correlated with liver histopathology and compared to a simple monoexponential model. The presence of a major, fast diffusing component and a minor, slow diffusing component was demonstrated. With increasing hepatic fibrosis, the fractional contribution of the fast diffusing component decreased, as did the diffusion coefficient of the fast diffusing component. Strong correlation between the degrees of liver fibrosis and a two-predictor regression model incorporating parameters of the biexponential model was found. Using Akaike's Information Criterion analyses, the biexponential model resulted in an improved fit of the high b-value diffusion data when compared to the monoexponential model.

Object recognition in clutter: cortical responses depend on the type of learning

Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI) of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter. We then tested subjects' recognition performance for both sets of objects in strong clutter. We found many brain regions that were differentially responsive to objects during object recognition depending on whether they were learned in strong or weak clutter. In particular, the responses of the left fusiform gyrus (FG) reliably reflected, on a trial-to-trial basis, subjects' object recognition performance for objects learned in the presence of strong clutter. These results indicate that the visual system does not use a single, general-purpose mechanism to cope with clutter. Instead, there are two distinct spatial patterns of activation whose responses are attributable not to the visual context in which the objects were seen, but to the context in which the objects were learned.