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Research on the neural mechanisms underlying brain asymmetry in patients with migraine patients using fMRI is insufficient. This study proposed using lateralized algorithms for functional connectivity and brain network topology and investigated changes in their lateralization in patients with migraine. In study 1, laterality indices of functional connectivity (LFunctionCorr) and brain network topological properties (LBetweennessCentrality, LDegree, and LStrength) were defined. Differences between migraineurs and normal subjects were compared at whole-brain, half-brain, and region levels. In study 2, laterality indices were used to classify migraine and were validated using independent samples and the segment method for repeatability. In study 3, abnormal brain regions related to migraine were extracted based on the classification results and differences analysis. Study 1 found no significant differences related to in for migraine at the whole-brain level; however, significant differences were identified at the half-brain level for the hemispheric lateralization of the LFunctionCorr, while 11 significantly different brain regions were also identified at the brain region level. Furthermore, the classification accuracy in study 2 was 0.9366. With repeated validation, the accuracy reached 0.8561. Furthermore, after extending the samples according to the segmentation strategy, the classification accuracies were improved to 0.9408 and 0.8585. Study 3 identified 10 crucial brain regions with asymmetrical specificity based on laterality indices distributed across the visual network, the frontoparietal control network, the default mode network, the salience/ventral attention network and the limbic system. The results revealed novel insights and avenues for research into the mechanisms of migraine asymmetry and showed that the laterality indices could be used as a potential diagnostic imaging marker for migraine., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Disorders of consciousness (DoC) are a group of conditions that affect the level of awareness and communication in patients. While neuroimaging techniques can provide useful information about the brain structure and function in these patients, most existing methods rely on a single modality for analysis and rarely account for brain injury. To address these limitations, we propose a novel method that integrates two neuroimaging modalities, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), to enhance the classification of subjects into different states of consciousness., Method and Results: The main contributions of our work are threefold: first, after constructing a dual-model individual graph using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), we introduce a brain injury mask mechanism that consolidates damaged brain regions into a single graph node, enhancing the modeling of brain injuries and reducing deformation effects. Second, to address over-smoothing, we construct a dual-level graph that dynamically construct a population-level graph with node features from individual graphs, to promote the clustering of similar subjects while distinguishing dissimilar ones. Finally, we employ a subgraph exploration model with task-fMRI data to validate the interpretability of our model, confirming that the selected brain regions are task-relevant in cognition. Our experimental results on data from 89 healthy participants and 204 patients with DoC from Huashan Hospital, Fudan University, demonstrate that our method achieves high accuracy in classifying patients into unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), or normal conscious state, outperforming current state-of-the-art methods. The explainability results of our method identified a subset of brain regions that are important for consciousness, such as the default mode network, the salience network, the dorsal attention network, and the visual network. Our method also revealed the relationship between brain networks and language processing in consciousness, and showed that language-related subgraphs can distinguish MCS from UWS patients., Conclusion: We proposed a novel graph learning method for classifying DoC based on fMRI and DTI data, introducing a brain injury mask mechanism to effectively handle damaged brains. The classification results demonstrate the effectiveness of our method in distinguishing subjects across different states of consciousness, while the explainability results identify key brain regions relevant to this classification. Our study provides new evidence for the role of brain networks and language processing in consciousness, with potential implications for improving the diagnosis and prognosis of patients with DoC., (© 2024. The Author(s).)

Objective: To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features., Methods: We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves., Results: The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839-0.898) and 0.847 (95% CI: 0.787-0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration., Conclusion: This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC., Competing Interests: Declaration of competing interest The authors have no relevant conflicts of interest to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Muscle hyperemia in exercise is usually the combined result of increased cardiac output and local muscle vasodilation, with the latter reflecting muscle's capacity for increased blood perfusion to support exercise. In this study, we aim to quantify muscle's vasodilation capability with dynamic BOLD imaging. A deoxyhemoglobin-kinetics model is proposed to analyze dynamic BOLD signals acquired during exercise recovery, deriving a hyperemia index (HI) for a muscle group of interest. We demonstrated the method's validity with calf muscles of healthy subjects who performed plantar flexion for muscle stimulation. In a test with exercise load incrementally increasing from 0 to 16 lbs., gastrocnemius HI showed considerable variance among the 4 subjects, but with a consistent trend, i.e. low at light load (e.g. 0-6 lbs) and linearly increasing at heavy load. The high variability among different subjects was confirmed with the other 10 subjects who exercised with a same moderate load of 8 lbs., with coefficient of variance among subjects' medial gastrocnemius 87.8%, lateral gastrocnemius 111.8% and soleus 132.3%. These findings align with the fact that intensive exercise induces high muscle hyperemia, but a comparison among different subjects is hard to make, presumably due to the subjects' different rate of oxygen utilization. For the same 10 subjects who exercised with load of 8 lbs., we also performed dynamic contrast enhanced (DCE) MRI to measure muscle perfusion (F). With a moderate correlation of 0.654, HI and F displayed three distinctive responses of calf muscles: soleus of all the subjects were in the cluster of low F and low HI, and gastrocnemius of most subjects had high F and either low or high HI. This finding suggests that parameter F encapsulates blood flow through vessels of all sizes, but BOLD-derived HI focuses on capillary flow and therefore is a more specific indicator of muscle vasodilation. In conclusion, the proposed hyperemia index has the potential of quantitatively assessing muscle vasodilation induced with exercise., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Molecular magnetic resonance imaging (mMRI) of biomarkers is essential for accurate cancer detection in precision medicine. However, the current clinically used contrast agents provide structural magnetic resonance imaging (sMRI) information only and rarely provide mMRI information. Here, a tumor-specific furin-catalyzed nanoprobe (NP) was reported for differential diagnosis of malignant breast cancers (BCs) in vivo . This NP with a compact structure of Fe 3 O 4 @Gd-DOTA NPs (FFG NPs) contains an "always-on" T 2 -weighted MR signal provided by the magnetic Fe 3 O 4 core and a furin-catalyzed enhanced T 1 -weighted MR signal provided by the Gd-DOTA moiety. The FFG NPs were found to produce an activated T 1 signal in the presence of furin catalysis and an "always-on" T 2 signal, providing mMRI and sMRI information simultaneously. Ratiometric mMRI:sMRI intensity can be used for differential diagnosis of malignant BCs MDA-MB-231 and MCF-7, where the furin levels relatively differ. The proposed probe not only provides structural imaging but also enables real-time molecular differential visualization of BC through enzymatic activities of cancer tissues.

Purpose: This study aims to assess the diagnostic performance of dual-energy computed tomography (DECT) on lumbar paravertebral muscles fat infiltration (PMFI) in participants with low back pain (LBP)., Method: In this prospective study, 21 participants with LBP were performed with noncontrast DECT scans within 1 week after magnetic resonance (MR) examinations. The assessment was based on the selected region of interest obtained from the paravertebral L1/L2-L5/S1 muscle. On visual evaluation, PMFI was assessed at DECT virtual monoenergetic images (80 keV) with Goutallier classification system using MR results as a reference. Quantitative parameters fat fraction, CT number, ΔCT number (difference of CT number at 140 and 80 keV), and optimal cutoff values above the indicators between MR adjacent grades were measured., Results: In this study, 582 ROIs from 21 participants (mean age, 60 ± 16 years old; 15 females) were evaluated. Sensitivity, specificity, and accuracy of readers 1 and 2 at severe grade (grades 3 and 4) were 67% and 85% (22 and 28 of 33), 99% and 99% (159 and 160 of 161), and 93% and 97% (181 and 188 of 194), respectively. Interobserver reliability was high with κ = 0.85 (p < 0.001). For DECT quantification parameters, significance was all represented between five grades (all p < 0.01). The area under the curve of indicators for discrimination between severe (grades 3 and 4) and normal and moderate (grades 0, 1, and 2) grades were > 0.80 (p < 0.001)., Conclusions: DECT was a promising qualitative and quantitative imaging technique to assess lumbar PMFI in participants with LBP and could provide accurate quantification for different fat infiltration (FI) degrees. Moreover, visual DECT assessment could excellently distinguish severe from normal and moderate FI of MR grades., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Accurate detection of hepatic hydrogen sulfide (H 2 S) to monitor H 2 S-related enzymes' activity is critical for acute hepatitis diagnosis, but remains a challenge due to the dynamic and transient nature of H 2 S. Here, we report a H 2 S-activatable near-infrared afterglow/MRI bimodal probe F1-GdNP, which shows an "always-on" MRI signal and "off-on" afterglow signal toward H 2 S. F1-GdNP shows fast response, high sensitivity and specificity toward H 2 S, permitting afterglow imaging of H 2 S and evaluation of cystathionine γ-lyase (CSE)'s activity in living mice. We further employ the high spatial-resolution MRI signal of F1-GdNP to track its delivery and accumulation in liver. Importantly, F1-GdNP offers a high signal-to-background ratio (SBR=86.2±12.0) to sensitively report on the increased hepatic H 2 S level in the acute hepatitis mice via afterglow imaging, which correlated well with the upregulated CSE activity in the liver, showcasing the good potential of F1-GdNP for monitoring of acute hepatitis process in vivo., (© 2021 Wiley-VCH GmbH.)

The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic processing method for the OB can be explained by its challenging properties (small size, location, and poor visibility on traditional MRI scans). Nonetheless, recent advances in MRI acquisition techniques and resolution have allowed raters to generate more reliable manual annotations. Furthermore, the high accuracy of deep learning methods for solving semantic segmentation problems provides us with an option to reliably assess even small structures. In this work, we introduce a novel, fast, and fully automated deep learning pipeline to accurately segment OB tissue on sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we designed a three-stage pipeline: (1) Localization of a region containing both OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized region through four independent AttFastSurferCNN - a novel deep learning architecture with a self-attention mechanism to improve modeling of contextual information, and (3) Ensemble of the predicted label maps. For this work, both OBs were manually annotated in a total of 620 T2w images for training (n=357) and testing. The OB pipeline exhibits high performance in terms of boundary delineation, OB localization, and volume estimation across a wide range of ages in 203 participants of the Rhineland Study (Dice Score (Dice): 0.852, Volume Similarity (VS): 0.910, and Average Hausdorff Distance (AVD): 0.215 mm). Moreover, it also generalizes to scans of an independent dataset never encountered during training, the Human Connectome Project (HCP), with different acquisition parameters and demographics, evaluated in 30 cases at the native 0.7 mm HCP resolution (Dice: 0.738, VS: 0.790, and AVD: 0.340 mm), and the default 0.8 mm pipeline resolution (Dice: 0.782, VS: 0.858, and AVD: 0.268 mm). We extensively validated our pipeline not only with respect to segmentation accuracy but also to known OB volume effects, where it can sensitively replicate age effects (β=-0.232, p<.01). Furthermore, our method can analyze a 3D volume in less than a minute (GPU) in an end-to-end fashion, providing a validated, efficient, and scalable solution for automatically assessing OB volumes., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Migraine seriously affects the physical and mental health of patients because of its recurrence and the hypersensitivity to the environment that it causes. However, the pathogenesis and pathophysiology of migraine are not fully understood. We addressed this issue in the present study using an autodynamic functional connectome model (A-DFCM) with twice-clustering to compare dynamic functional connectome patterns (DFCPs) from resting-state functional magnetic resonance imaging data from migraine patients and normal control subjects. We used automatic localization of segment points to improve the efficiency of the model, and intergroup differences and network metrics were analyzed to identify the neural mechanisms of migraine. Using the A-DFCM model, we identified 17 DFCPs-including 1 that was specific and 16 that were general-based on intergroup differences. The specific DFCP was closely associated with neuronal dysfunction in migraine, whereas the general DFCPs showed that the 2 groups had similar functional topology as well as differences in the brain resting state. An analysis of network metrics revealed the critical brain regions in the specific DFCP; these were not only distributed in brain areas related to pain such as Brodmann area 1/2/3, basal ganglia, and thalamus but also located in regions that have been implicated in migraine symptoms such as the occipital lobe. An analysis of the dissimilarities in general DFCPs between the 2 groups identified 6 brain areas belonging to the so-called pain matrix. Our findings provide insight into the neural mechanisms of migraine while also identifying neuroimaging biomarkers that can aid in the diagnosis or monitoring of migraine patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Weifang Nie et al.)

Radiotherapy occupies an essential position in curing and palliating a wide range of solid tumors based on DNA damage responses to eradicate cancer cells. However, the tumor microenvironment generally exhibits the characteristics of hypoxia and glutathione overexpression, which play a critical role in radioresistance, to prevent irreparable breaks to DNA and necrocytosis of cancer cells. Herein, polyethylene glycol (PEG) functionalized manganese ferrite nanoparticles (MnFe 2 O 4 -PEG) are designed to enable self-sufficiency of oxygen by continuously catalyzing the decomposition of endogenous hydrogen peroxide. Simultaneously, the nano-platform can consume GSH to reduce the loss of reactive oxygen species in radiotherapy and achieve better therapeutic effects at the cellular and animal levels. In addition, the MnFe 2 O 4 -PEG could act as an optimal T 1 - and T 2 -weighted contrast medium for tumor-specific magnetic resonance imaging. This work proposes a systematically administered radiosensitizer that can selectively reside in tumor sites via the enhanced permeability and retention effect to relieve hypoxia and reduce GSH concentration, combined with dual-mode magnetic resonance imaging, achieving precise and effective image-guided tumor therapy.

White matter hyperintensity (WMH) is a common finding in aging population and considered to be a contributor to cognitive decline. Our study aimed to characterize the spatial patterns of WMH in different severities and explore its impact on cognition and brain microstructure in non-demented elderly. Lesions were both qualitatively (Fazekas scale) and quantitatively assessed among 321 community-dwelled individuals with MRI scanning. Voxel- and atlas-based analyses of the whole-brain white matter microstructure were performed. The WMH of the same severities was found to occur uniformly with a specific pattern of lesions. The severity of WMH had a significant negative association with the performance of working and episodic memory, beginning to appear in Fazekas 3 and 4. The white matter tracts presented significant impairments in Fazekas 3, which showed brain-wide changes above Fazekas 4. Lower FA in the superior cerebellar peduncle and left posterior thalamic radiation was mainly associated with episodic memory, and the middle cerebellar peduncle was significantly associated with working memory. These results support that memory is the primary domain to be affected by WMH, and the effect may potentially be influenced by tract-specific WM abnormalities. Fazekas scale 3 might be the critical stage predicting a future decline in cognition.

Background: To investigate whether quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic parameters can be used to predict the pathologic stages of oral tongue squamous cell carcinoma (OTSCC)., Methods: For this prospective study, DCE-MRI was performed in participants with OTSCC from May 2016 to June 2017. The pharmacokinetic parameters, including K trans , K ep , V e , and V p , were derived from DCE-MRI by utilizing a two-compartment extended Tofts model and a three-dimensional volume of interest. The postoperative pathologic stage was determined in each patient based on the 8th AJCC cancer staging manual. The quantitative DCE-MRI parameters were compared between stage I-II and stage III-IV lesions. Logistic regression analysis was used to determine independent predictors of tumor stages, followed by receiver operating characteristic (ROC) analysis to evaluate the predictive performance., Results: The mean K trans , K ep and V p values were significantly lower in stage III-IV lesions compared with stage I-II lesions (p = 0.013, 0.005 and 0.011, respectively). K ep was an independent predictor for the advanced stages as determined by univariate and multivariate logistic analysis. ROC analysis showed that K ep had the highest predictive capability, with a sensitivity of 64.3%, a specificity of 82.6%, a positive predictive value of 81.8%, a negative predictive value of 65.5%, and an accuracy of 72.5%., Conclusion: The quantitative DCE-MRI parameter K ep can be used as a biomarker for predicting pathologic stages of OTSCC.

Aims: To investigate the correlation of impairment in skeletal muscle and heart in spontaneous type 2 diabetes mellitus (T2DM) rhesus monkeys using magnetic resonance image (MRI)., Methods: Fifteen T2DM monkeys and fourteen healthy control (HC) monkeys were included. The microcirculation of skeletal muscle [skeletal muscle blood flow (SMBF), skeletal muscle oxygen extraction fraction (SMOEF)] and the function and strain of heart were evaluated by MRI. Three regions of interests were chosen on the soleus muscle (SOL), gastrocnemius muscle (GAS) and tibialis anterior muscle (TA) for image analysis., Results: Eight T2DM monkeys and eight HC monkeys were obtained the full data. The SMBF reserves and SMOEF reserves were found significantly decreased in T2DM during inflation in SOL, GAS and TA muscles (all p < 0.05), and the SMBF reserves decreased during hyperemia in GAS and TA muscles (all p < 0.05). In these monkeys, the global peak longitudinal strain (longitudinal PS), peak systolic longitudinal strain rate (longitudinal PSSR) and peak diastolic longitudinal strain rate (longitudinal PDSR) were seen significantly different in T2DM compared to HC monkeys (all p < 0.05). The longitudinal PSSR was found negatively correlated with SMBF reserves in SOL, GAS and TA during inflation in all monkeys., Conclusions: The impaired microcirculation of skeletal muscle and the myocardial deformation were found in T2DM monkeys with normal ejection fraction. And a negative correlation was existed in the longitudinal PSSR and the SMBF reserves.

Granger causality (GC) is one of the most popular measures to investigate causality influence among brain regions and has been achieved significant results for exploring brain networks based on functional magnetic resonance imaging (fMRI). However, the predictors and order selection of conventional GC are based on linear models which result in such restrictions as poorly detection of nonlinearity and so on, in the application. This paper proposes a novel GC model called back propagation (BP) based kernel function Granger causality (BP&#95;KFGC), in which symplectic geometry is used for embedding dimension and fuzzy inference system for predicting time series. The proposed method doesn't depend on the prediction of the vector auto-regression model, so that time series don't need to be wide-sense stationary as linear GC and kernel GC. In addition, it is a multivariate approach which is applicable to both linear and nonlinear systems and eliminates the effects of latent variables. The performance of the new method is evaluated and compared with linear GC, partial GC, neural network GC and kernel GC by simulated data with multiple adjustments to the nonlinearity. The results show that BP&#95;KFGC outperforms the other four methods in detecting both linear and nonlinear causalities. Furthermore, we applied BP&#95;KFGC to construct directed weight network (DWN) of Alzheimer's disease (AD) patients and health controls (HCs), and then nine graph-based features of DWN were used for classification by the classifier of support vector machine with radial basis kernel function. The accuracy of 95.89%, sensitivity of 93.31%, and specificity of 94.97% were achieved which may provide an auxiliary mean for the clinical diagnosis of AD.

Migraine is a chronic dysfunction characterized by recurrent pain, but its pathogenesis is still unclear. As a result, more and more methods have been focused on the study of migraine in recent years, including functional magnetic resonance imaging (fMRI), which is a mainstream technique for exploring the neural mechanisms of migraine. In this paper, we systematically investigated the fMRI functional connectivities (FCs) between large-scale brain networks in migraine patients from the perspective of multi-channel hierarchy, including static and dynamic FCs of group and individual levels, where the brain networks were obtained using group independent component analysis. Meanwhile, the corresponding topology properties of static and dynamic FCs networks in migraine patients were statistically compared with those in healthy controls. Furthermore, a graph metrics based method was used to detect the potential brain functional connectivity states in dynamic FCs at individual and group levels, and the corresponding topology properties and specificity of these brain functional connectivity states in migraine patients were explored compared with these in healthy controls. The results showed that the dynamic FCs and corresponding global topology properties among nine large-scale brain networks involved in this study have significant differences between migraine patients and healthy controls, while local topological properties and dynamic fluctuations were easily affected by window-widths. Moreover, the implicit dynamic functional connectivity patterns in migraine patients presented specificity and consistency under different window-widths, which suggested that the dynamic changes in FCs and topology structure between them played a key role in the brain functional activity of migraine. Therefore, it may be provided a new perspective for the clinical diagnosis of migraine., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Independent component analysis (ICA) has become a widely used method for functional magnetic resonance imaging (fMRI) data analysis. However, spatial ICA usually performs better than temporal ICA with regard to the stability and accuracy of functional connectivity detection, and temporal ICA is often not feasible when it is applied to the analysis of real fMRI data of the whole brain because of the excessive spatial dimensions. In this paper, to overcome these problems, we propose a sub-packet constrained temporal ICA (SCTICA) method to take advantage of the a priori information using a multi-objective optimization framework with the Newton iterative algorithm. Moreover, a splitting strategy is presented to improve the feasibility of the temporal ICA for whole brain fMRI data analysis. The experimental results of real data show that the splitting strategy improved the ability of the temporal ICA to analyze whole brain fMRI data. Furthermore, the experimental results also demonstrated that the proposed SCTICA method can not only improve the stability of the temporal ICA, but can also improve the functional connectivity detection ability compared with the classical ICA and ICA with a priori information methods. In brief, the proposed SCTICA method overcomes the problem that prevents temporal ICA from being applied to fMRI data of the whole brain, and the functional connectivity detection performance is greatly improved compared with that of traditional methods., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Magnetic Resonance Elastography (MRE) detects induced periodic motions in biological tissues allowing maps of tissue mechanical properties to be derived. In-vivo MRE is commonly performed at frequencies of 30-100 Hz using external actuation, however, using cerebro-vascular pulsation at 1 Hz as a form of intrinsic actuation (IA-MRE) eliminates the need for external motion sources and simplifies data acquisition. In this study a hydraulic actuation system was developed to drive 1 Hz motions in gelatin as a tool for investigating the performance limits of IA-MRE image reconstruction under controlled conditions. Quantitative flow (QFLOW) MR techniques were used to phase encode 1 Hz motions as a function of gradient direction using 3D or 4D acquisition; 4D acquisition was twice as fast and yielded comparable motion field and concomitant image reconstruction results provided the motion signal was sufficiently strong. Per voxel motion noise floor corresponded to a displacement amplitude of about 20-30 μm. Signal to noise ratio (SNR) was 94 ± 17 for 3D and dropped to 69 ± 10 for the faster 4D acquisition, but yielded octahedral shear stress and shear modulus maps of high quality that differed by only about 20% on average. QFLOW measurements in gel phantoms were improved significantly by adding Mn(II) to mimic relaxation rates found in brain. Overall, the hydraulic 1 Hz actuation system when coupled with 4D sequence acquisition produced a fast reliable approach for future IA-MRE phantom evaluation and contrast detail studies needed to benchmark imaging performance., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Compared with independent component analysis (ICA), constrained ICA (CICA) has unique advantages in functional magnetic resonance image (fMRI) data analysis by introducing some priori information into the estimation process. However, there are still some controversies in the current CICA methods, such as how to choose the threshold parameter to restrain the similarity, and how to reduce the accuracy requirements for a priori information. In this paper, we propose a new constrained spatiotemporal ICA (CSTICA) method based on the framework of multi-objective optimization, where the inequality constraint of the traditional CICA method is transformed into the objective optimization function of the CSTICA, and both temporal and spatial a priori information are included simultaneously. The simulated, hybrid, and real fMRI data experiments are designed to evaluate the performance of the proposed CSTICA method in comparison with the classical ICA and CICA methods. Compared with the traditional CICA methods, the CSTICA has circumvented the problem of threshold parameter selection. Furthermore, the experimental results demonstrate that the source recovery ability of the CSTICA has been improved to a certain extent especially in the cases of a priori information with low accuracies. Meanwhile, the results also indicate that the CSTICA reduces dependency on the accuracy of a priori information.

Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis.

Objective: To determine the diagnostic performance of volumetric quantitative dynamic contrast-enhanced MRI (qDCE-MRI) in differentiation between malignant and benign breast lesions., Methods: DCE-MRI was performed in 124 patients with 136 breast lesions. Quantitative pharmacokinetic parameters K trans , K ep , V e , V p and semi-quantitative parameters TTP, MaxCon, MaxSlope, AUC were obtained by using a two-compartment extended Tofts model and three-dimensional volume of interest. Morphologic features (lesion size, margin, internal enhancement pattern) and time-signal intensity curve (TIC) type were also assessed. Logistic regression analysis was used to determine predictors of malignancy, followed by receiver operating characteristics (ROC) analysis to evaluate the diagnostic performance., Results: qDCE parameters (K trans , K ep , V p , TTP, MaxCon, MaxSlope and AUC), morphological parameters and TIC type were significantly different between malignant and benign lesions (P≤0.001). Multivariate logistic regression analyses showed that K trans , K ep , MaxSlope, size, margin and TIC type were independent predictors of malignancy. The diagnostic accuracy of logistic models based on qDCE parameters alone, morphological features plus TIC type, and all parameters combined was 94.9%, 89.0%, and 95.6% respectively., Conclusion: qDCE-MRI can be used to improve diagnostic differentiation between benign and malignant breast lesions in relation to morphology and kinetic analysis., Key Points: • qDCE-MRI parameters are useful for discriminating between malignant and benign breast lesions. • K trans , K ep and MaxSlope were independent predictors of breast malignancy. • qDCE-MRI has a better diagnostic ability than morphology and kinetic analysis. • qDCE-MRI can be used to improve the diagnostic accuracy of breast malignancy.

Background: Cardiac magnetic resonance (CMR) T1 mapping and tissue-tracking strain analysis are useful quantitative techniques that can characterize myocardial tissue and mechanical alterations, respectively, in patients with early diabetic cardiomyopathy. The purpose of this study was to assess the left ventricular myocardial T1 value, extracellular volume fraction (ECV), and systolic strain in asymptomatic patients with type 2 diabetes mellitus (T2DM) and their underlying relationships with clinical parameters., Methods: We recruited 50 T2DM patients (mean age: 55 ± 7 years; 28 males) and 32 sex-, age-and BMI-matched healthy volunteers to undergo contrast-enhanced CMR examinations. The myocardial native T1, post-contrast T1 and ECV values of the left ventricle were measured from T1 and ECV maps acquired using the modified Look-Locker inversion recovery technique. The left ventricular global systolic strain and the strain rates were evaluated using routine cine images and tissue-tracking analysis software. The baseline clinical and biochemical indices were collected before the CMR examination., Results: The myocardial ECV and native T1 values were significantly higher in the diabetic patients than in the controls. (ECV: 27.4 ± 2.5% vs. 24.6 ± 2.2%, p < 0.001; native T1: 1026.9 ± 30.0 ms vs. 1011.8 ± 26.0 ms, p = 0.022). However, the left ventricular global systolic strain, strain rate, volume, myocardial mass, ejection fraction, and left atrial volume were similar between the diabetic patients and the healthy controls. In the diabetic patients, the native T1 values were independently correlated with the hemoglobin A1c levels (standardized β = 0.368, p = 0.008). The ECVs were independently associated with the hemoglobin A1c levels (standardized β = 0.389, p = 0.002), angiotensin-converting enzyme inhibitor (ACEI) treatment (standardized β = - 0.271, p = 0.025) and HCT values (standardized β = - 0.397, p = 0.001)., Conclusions: Type 2 diabetes mellitus patients with normal myocardial systolic strain exhibit increased native T1 values and ECVs indicative of myocardial extracellular interstitial expansion, which might be related to poor glycemic control. The amelioration of myocardial interstitial matrix expansion might be associated with ACEI treatment. A valid assessment of the association of glucose control and ACEI treatment with myocardial fibrosis requires notably larger trials.

Background and Objective: With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages., Methods: In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively., Results: The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains., Conclusions: These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Microvascular lesions of the body are one of the most serious complications that can affect patients with type 2 diabetes mellitus. The blood-brain barrier (BBB) is a highly selective permeable barrier around the microvessels of the brain. This study investigated BBB disruption in diabetic rhesus monkeys using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Multi-slice DCE-MRI was used to quantify BBB permeability. Five diabetic monkeys and six control monkeys underwent magnetic resonance brain imaging in 3 Tesla MRI system. Regions of the frontal cortex, the temporal cortex, the basal ganglia, the thalamus, and the hippocampus in the two groups were selected as regions of interest to calculate the value of the transport coefficient K trans using the extended Tofts model. Permeability in the diabetic monkeys was significantly increased as compared with permeability in the normal control monkeys. Histopathologically, zonula occludens protein-1 decreased, immunoglobulin G leaked out of the blood, and nuclear factor E2-related factor translocated from the cytoplasm to the nuclei. It is likely that diabetes contributed to the increased BBB permeability., (Copyright © 2016 Elsevier Inc. All rights reserved.)

The relationship between abdominal adiposity and disc degeneration remains largely uninvestigated. Here, we investigated the association between abdominal adipose tissue thickness and lumbar disc degeneration in a cross-sectional study of 2415 participants from The Second Affiliated Hospital of Wenzhou Medical University. All subjects were scanned with a 3T Magnetic Resonance Imaging system to evaluate the degree of lumbar disc degeneration. Multiple logistic regression analysis revealed that men in the highest quartiles for abdominal diameter (AD), sagittal diameter (SAD), and ventral subcutaneous thickness (VST) were at higher odds ratio for severe lumbar disc degeneration than men in the lowest quartiles. The adjusted model revealed that women in the highest quartiles for AD and SAD were also at higher odds ratio for severe lumbar disc degeneration than women in the lowest quartiles. Our results suggest that abdominal obesity might be one of underlying mechanisms of lumbar disc degeneration, and preventive strategies including weight control could be useful to reduce the incidence of lumbar disc degeneration. Prospective studies are needed to this confirm these results and to identify more deeper underlying mechanisms.

Objective: Many studies have shown that the independence assumption in the widely-used ICAs is not adaptive enough for brain functional networks (BFN) detection due to the complex brain hemodynamics, functional integration, artifacts embedded in functional magnetic resonance imaging (fMRI) data, etc. In this paper, inspired by sparse coding behavior of human brain, we propose an effective BFNs detection model, called sparse dictionary learning separation (SDLS)., Methods: In the SDLS, facing the dilemma of huge training samples in sparse learning, an efficient spatial-domain data reduction algorithm was first designed to sharply alleviate the training cost and suppress noise. Then, an improved K singular value decomposition was proposed to speed up the correct convergence of the dictionary learning process. Furthermore, considering the variant degrees of functional integration and sparsity of BFNs across different fMRI datasets, a minimum description length-based framework was proposed to formulate two key factors, i.e., the dictionary mutual incoherence level and sparsity level, self-adaptively resulting in effective temporal dynamics model. Finally, a least-square-based functional network reconstruction was presented to extract the final BFNs., Results: The simulated and real data experiments demonstrated that SDLS had the superiority in the spatial/temporal sources identification, and stronger spatial robustness against the variant smoothing kernels, in contrast to ICAs., Conclusion: SDLS was a novel data-driven BFN separation model, which had an overall consideration of multiple factors, e.g., huge samples dilemma, artifacts removal, and variant degrees of functional integration and sparsity of BFNs., Significance: SDLS as an extension to current fMRI analysis methods was a promising model, which declared the advantage of sparsity.

Background: To report the diversity of MRI features of brainstem encephalitis (BE) induced by Enterovirus 71. This is supported by implementation and testing of our new classification scheme in order to improve the diagnostic level on this specific disease., Methods: Neuroimaging of 91 pediatric patients who got EV71 related BE were hospitalized between March, 2010 to October, 2012, were analyzed retrospectively. All patients underwent pre- and post-contrast MRI scan. Thereafter, 31 patients were randomly called back for follow-up MRI study during December 2013 to August 2014. The MRI signal patterns of BE primary lesion were analyzed and classified according to MR signal alteration at various disease stages. Findings in fatal and non-fatal cases were compared, and according to the MRI scan time point during the course of this disease, the patients' conditions were classified as 1) acute stage, 2) convalescence stage, 3) post mortem stage, and 4) long term follow-up study., Results: 103 patients were identified. 11 patients did not undergo MRI, as they died within 48 hours. One patient died on 14th day without MR imaging. 2 patients had postmortem MRI. Medical records and imaging were reviewed in the 91 patients, aged 4 months to 12 years, and two cadavers who have had MRI scan. At acute stage: the most frequent pattern (40 patients) was foci of prolonged T1 and T2 signal, with (15) or without (25) contrast enhancement. We observed a novel pattern in 4 patients having foci of low signal intensity on T2WI, with contrast enhancement. Another pattern in 10 patients having foci of contrast enhancement without abnormalities in T1WI or T2WI weighted images. Based on 2 cases, the entire medulla and pons had prolonged T1 and T2 signal, and 2 of our postmortem cases demonstrated the same pattern. At convalescence stage, the pattern observed in 4 patients was foci of prolonged T1 and T2 signal without contrast enhancement. Follow-up MR study of 31 cases showed normal in 26 cases, and demonstrated foci of prolonged T1 and T2 signal with hyper-intensity on FLAIR in 3 cases, or of prolonged T1 and T2 signal with hypo-intensity on FLAIR in 2 cases. Most importantly, MR findings of each case were thoroughly investigated and classified according to phases and MRI signal alteration., Conclusions: This study has provided enhanced and useful information for the MRI features of BE induced by EV71, apart from common practice established by previous reports. In addition, a classification scheme that summarizes all types of features based on the MRI signal at the four different stages of the disease would be helpful to improve the diagnostic level., Competing Interests: The authors have declared that no competing interests exist.

Independent component analysis aiming at detecting the functional connectivity among discrete cortical brain regions has been extensively used to explore the functional magnetic resonance imaging data. Although the independent components (ICs) were with relatively high quality, the noise embedding in ICs has a great impact on the true active/inactive region inference and the reproducibility, in postprocessing stage, e.g., the extraction of statistical parametrical maps (SPMs). In this paper, a novel brain network enhancement model (BNEM) is proposed, which mainly consists of two key techniques: 1) 3-D wavelet noise filter (3DWNF) for the meaningful ICs, which greatly suppresses noise and enforces the real activation inference of SPMs; and 2) a spatial reproducibility enhancement algorithm (SREA), aiming to improve the reproducibility of SPMs. The simulated experiment demonstrated that the postfiltering signals by 3DWNF were with higher correlation and less normalized mean square error to the ground truths than the prefiltering ones; SREA could further enhance the quality of most postfiltering ones, preserving the consistency with 3DWNF. The real data experiments also revealed that 1) 3DWNF could lead to more accurate preservation of the true positive voxels by correctly identifying the high proportionally misclassified voxels of the nonenhanced SPMs; 2) SREA could further improve the classification accuracy of the active/inactive voxels of SPMs corresponding to the 3DWNF denoised ICs; and 3) both 3DWNF and SREA contribute to the reproducibility enhancement of the reproduced SPMs by BNEM. Thus, BNEM is expected to have wide applicability in the neuroscience and clinical domain.

Background: The objective of this study is to assess standardized histograms of signal intensities of T1 signal and T2 signal on sagittal view without enhancement during (1) acute stage, and (2) convalescence stage of pediatric patients with Enterovirus 71 related brainstem encephalitis (BE), and with respect to (3) healthy normal., Methods: Our subjects were hospitalized between March 2010 and October 2012, and underwent pre- and post-contrast MRI studies. The research question to be answered is whether the comparison of the MRI image intensity histograms and relevant statistical quantification can add new knowledge to the diagnosis of BE patients. So, both 25 cases in acute stage with prolonged T1 and T2 signal, without enhancement, and 13 cases in convalescence stage were introduced. In additional, a healthy group with 25 cases was recruited for comparison., Results: MRI signal intensity histogram changes of the lesions were compared at the acute and convalescence stages of the disease. Our preliminary results suggest that standardized histograms of signal intensities and their statistical properties are able to provide diagnostic information for the clinical assessment of the disease. Different stages pertaining to the histogram plots comparison showed that overall T1 signal intensity values increase as we traverse from the acute stage to the convalescence stage. And then for the healthy subjects, the T2 signal intensity values changed their magnitudes in a reverse direction. However, exceptions of this can happen in four cases where the primary lesions occurred in the brainstem that developed encephalomalacia resulting in a lower signal in T1WI and higher signal in T2WI. Statistical analysis revealed there was significant difference of T1 signal intensity among the three groups; and also, the T2 signal intensity was lower than other two groups., Conclusions: Standardized histogram of T1 and T2 intensity provide valuable and useful information for disease diagnosis and evaluation, which can potentially help medical doctors to save the lives of children.

Group-independent component analysis (GICA) is a well-established blind source separation technique that has been widely applied to study multi-subject functional magnetic resonance imaging (fMRI) data. The group-independent components (GICs) represent the commonness of all of the subjects in the group. Similar to independent component analysis on the single-subject level, the performance of GICA can be improved for multi-subject fMRI data analysis by incorporating a priori information; however, a priori information is not always considered while looking for GICs in existing GICA methods, especially when no obvious or specific knowledge about an unknown group is available. In this paper, we present a novel method to extract the group intrinsic reference from all of the subjects of the group and then incorporate it into the GICA extraction procedure. Comparison experiments between FastICA and GICA with intrinsic reference (GICA-IR) are implemented on the group level with regard to the simulated, hybrid and real fMRI data. The experimental results show that the GICs computed by GICA-IR have a higher correlation with the corresponding independent component of each subject in the group, and the accuracy of activation regions detected by GICA-IR was also improved. These results have demonstrated the advantages of the GICA-IR method, which can better reflect the commonness of the subjects in the group., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Background: Researches declared that the super-Gaussian property contributed to the success of some spatial independent component analysis (ICA) algorithms in brain fMRI source separation (e.g., Infomax and FastICA), which implied that sparse approximation transforming the sources (super-Gaussian or Gaussian-like) with stronger super-Gaussian feature would possibly improve the separation performance of these algorithms., New Method: This paper presented a novel wavelet-shrinkage based ICA (WASICA) model, an extension of our previous SACICA, for single-subject analysis. In contrast, two main aspects had been effectively enhanced: (1) sparse approximation coefficients set formation, made up of two sub-procedures: the wavelet-shrinkage of wavelet packet (WP) tree nodes, and the automatic nodes selection and integration based on the relative WP energy; (2) ICA-based decomposition and reconstruction, composed of temporal dynamics extraction using ICA, WP reconstruction based on the sparse approximation coefficients set and least-square-based functional networks reconstruction., Results: The wavelet-shrinkage and the automatic nodes selection and integration simultaneously transformed both the mixtures and underlying sources into effective sparse approximation coefficients sets, exhibiting stronger super-Gaussian distribution; WP projected-back approximation with nuisance-exclusion contributed to networks reconstruction., Comparison With Existing Methods: Simulation 1 revealed WASICA successfully recovered super-Gaussian and some Gaussian-like sources. Simulation 2 and hybrid data experiments showed that WASICA with good temporal performance had stronger source recovery ability and signal detection sensitivity spatially than FastICA, Infomax and SACICA did; the higher intra-consistency in task-related experiments denoted WASICA occupied stronger spatial robustness against smooth kernels., Conclusions: WASICA was a promising brain signal separation model with charming spatial-temporal performance., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The goal of our study is to develop a fast parallel implementation of group independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data using graphics processing units (GPU). Though ICA has become a standard method to identify brain functional connectivity of the fMRI data, it is computationally intensive, especially has a huge cost for the group data analysis. GPU with higher parallel computation power and lower cost are used for general purpose computing, which could contribute to fMRI data analysis significantly. In this study, a parallel group ICA (PGICA) on GPU, mainly consisting of GPU-based PCA using SVD and Infomax-ICA, is presented. In comparison to the serial group ICA, the proposed method demonstrated both significant speedup with 6-11 times and comparable accuracy of functional networks in our experiments. This proposed method is expected to perform the real-time post-processing for fMRI data analysis., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first. work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method.

Clustering analysis has been widely used to detect the functional connectivity from functional magnetic resonance imaging (fMRI) data. However, it has some limitations such as enormous computer memory requirement, and difficulty in estimating the number of clusters. In this study, in order to effectually resolve the deficiencies mentioned above, we have proposed a novel approach (SAAPC) for fMRI data analysis, which combines sparsity, an effective assumption for analyzing fMRI signal, with affinity propagation clustering (APC). The SAAPC method is composed of three parts: to obtain the sparse approximation coefficients set through wavelet packet decomposition and sparsity measuring and selection, which contributes a lot in the brain functional connectivity detection accuracy; to implement a split APC algorithm, which is put forward in this paper to overcome the computer memory shortage problem and to reduce the time cost in basic APC; to reconstruct the source signal by unmixing the mixed fMRI data using the time courses which are derived from the ultimate exemplars. In the task-related experiments, we can see that SAAPC is more accurate to detect the functional networks than basic APC, and it significantly reduces the time cost relative to basic APC. In addition, in the resting-state data experiments, the SAAPC method can successfully identify typical resting-state networks from the resting-state data set, while this performance is seldom reported by the classical cluster method and the basic APC method. This proposed clustering analysis method is expected to have wide applicability., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data to evaluate the functional connectivity, which assumes that the sources of functional networks are statistically independent. Recently, many researchers have demonstrated that sparsity is an effective assumption for fMRI signal separation. In this research, we present a sparse approximation coefficient-based ICA (SACICA) model to analyse fMRI data, which is a promising combination model of sparse features and an ICA technique. The SACICA method consists of three procedures. The wavelet packet decomposition procedure, which decomposes the fMRI data into wavelet tree nodes with different degrees of sparsity, is first. Then, the sparse approximation coefficients set formation procedure, in which an effective Lp norm is proposed to measure the sparse degree of the distinct wavelet tree nodes, is second. The ICA decomposition and reconstruction procedure, which utilises the sparse approximation coefficients set of the fMRI data, is last. The hybrid data experimental results demonstrated that the SACICA method exhibited the stronger spatial source reconstruction ability with respect to the unsmoothed fMRI data and better detection sensitivity of the functional signal on the smoothed fMRI data than the FastICA method. Furthermore, task-related experiments also revealed that SACICA was not only effective in discovering the functional networks but also exhibited a better detection sensitivity of the visual-related functional signal. In addition, the SACICA combined with Fast-FENICA proposed by Wang et al. (2012) was demonstrated to conduct the group analysis effectively on the resting-state data set., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Brain Cortical surface registration is required for inter-subject studies of functional and anatomical data. Harmonic mapping has been applied for brain mapping, due to its existence, uniqueness, regularity and numerical stability. In order to improve the registration accuracy, sculcal landmarks are usually used as constraints for brain registration. Unfortunately, constrained harmonic mappings may not be diffeomorphic and produces invalid registration. This work conquer this problem by changing the Riemannian metric on the target cortical surface o a hyperbolic metric, so that the harmonic mapping is guaranteed to be a diffeomorphism while the landmark constraints are enforced as boundary matching condition. The computational algorithms are based on the Ricci flow method and yperbolic heat diffusion. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic, with higher qualities in terms of landmark alignment, curvature matching, area distortion and overlapping of region of interests.

For resting-state fMRI data, independent component analysis (ICA) is an excellent method which enables the decomposition of high-dimensional data into discrete spatial and temporal components. Fully exploratory network ICA (FENICA), a fully automated and purely data-driven ICA-based analysis for group assessment of resting-state networks, was proposed by Schöpf et al. (2010). FENICA is a novel and effective group assessment method, but it is not without limitations, such as those related to memory and time costs in running. Here we present Fast-FENICA, which is based on an energy sifting algorithm for interested networks, a linear candidate networks formation strategy and a correlation coefficients ranking algorithm of network matrix. It is demonstrated that the energy sifting algorithm for interested networks and linear candidate networks formation strategy can transform the stubborn computing time and memory cost limitations of FENICA from a quadratic level to a linear level and thus speed up the group evaluation. Furthermore, the correlation coefficients ranking algorithm can further increase the calculation speed and float up the consistent networks effectively. In comparison to FENICA, the hybrid data and true data experimental results demonstrate that Fast-FENICA not only contributes to the practicability and efficiency without decreasing the detecting ability of functional networks, but also ranks the common functional networks based on the whole spatial consistency at a group level. This proposed effective group analysis method is expected to have wide applicability., (Copyright © 2012 Elsevier B.V. All rights reserved.)

There is an increasing body of evidence pointing to a relationship between personality and brain markers. The purpose of this study was to identify the associations between personality dimensions of extraversion and neuroticism and the local synchronization of spontaneous blood oxygen level-dependent (BOLD) activity assessed by regional homogeneity (ReHo) approach. Our results revealed the significant negative correlation between neuroticism and ReHo in the left middle frontal gyrus, providing evidence for the left frontal activation involved in pleasant emotion. ReHo was correlated negatively with extraversion in the medial prefrontal cortex (MPFC), an important portion of the default mode network (DMN), thus further indicating the relationship between DMN and personality. In addition, ReHo in the insula, cerebellum and cingulate gyrus was correlated positively with extraversion, suggesting the associations between individual difference in extraversion and specific brain regions involved in affective processing. These findings shed light on the important relationship between the synchronization of spontaneous fluctuations and personality dimensions of extraversion and neuroticism, which provide further evidence for the neural underpinning of individual difference in personality traits., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Adolescent Idiopathic Scoliosis (AIS) characterized by the 3D spine deformity affects about 4% schoolchildren worldwide. One of the prominent theories of the etiopathogenesis of AIS was proposed to be the poor postural balance control due to the impaired vestibular function. Thus, the morphometry of the vestibular system (VS) is of great importance for studying AIS. The VS is a genus-3 structure situated in the inner ear and consists of three semicircular canals lying perpendicular to each other. The high-genus topology of the surface poses great challenge for shape analysis. In this work, we propose an effective method to analyze shapes of high-genus surfaces by considering their geodesic spectra. The key is to compute the canonical hyperbolic geodesic loops of the surface, using the Ricci flow method. The Fuchsian group generators are then computed which can be used to determine the geodesic spectra. The geodesic spectra effectively measure shape differences between high-genus surfaces up to the hyperbolic isometry. We applied the proposed algorithm to the VS of 12 normal and 15 AIS subjects. Experimental results show the effectiveness of our algorithm and reveal statistical shape difference in the VS between right-thoracic AIS and normal subjects.

Independent component analysis (ICA) decomposes fMRI data into spatially independent maps and their corresponding time courses. However, distinguishing the neurobiologically and biophysically reasonable components from those representing noise and artifacts is not trivial. We present a simple method for the ranking of independent components, by assessing the resemblance between components estimated from all the data, and components estimated from only the odd- (or even-) numbered time points. We show that the meaningful independent components of fMRI data resemble independent components estimated from downsampled data, and thus tend to be highly ranked by the method.

Magnetic resonance imaging is a non-invasive imaging method that offers high-resolution, high quality in vivo visualization for medical diagnostics. Magnetic nanoparticles (NPs) containing Mn2+ offer an attractive alternative to Gd-based molecular contrast agents for T1 MRI. In this work, we show that highly anisotropic MnO NPs can be generated from a single precursor using simple synthetic protocols. These anisotropic morphologies offer better contrast augmentation when compared to spherical MnO NPs of similar sizes. [ABSTRACT FROM AUTHOR]

Objective: To investigate the changes in paraspinal muscle cross-sectional area (CSA) and composition, using the digital data from lumbar spine MRIs of patients with acute and chronic low back pain (LBP). Methods: In total, 178 patients with unilateral LBP who had lumbar MRI examination were recruited. The data were obtained by a retrospective documentation audit. The CSAs and mean signal intensities of the bilateral paraspinal muscles [psoas major (PM), quadratus lumborum, multifidus (MF) and erector spinae (ES)] were measured, and the percentage of fat infiltration was calculated. The data between the painful side and non-painful side were compared, and between-group comparisons were tested. 42 patients with chronic unilateral LBP could indicate the problem level, and the CSA and mean signal intensity of the MF muscle were analysed at the problem level, and one vertebral above and one vertebral level below the problem level. Results: The CSAs of the PM and ES muscles were significantly decreased in the acute LBP group, while in the chronic LBP group, significant reduction in CSA was found in the MF and ES muscles on the painful side compared with the non-painful side. The mean signal intensity and fat content of the ES muscle on the painful side in the chronic LBP group was significantly higher than that on the painful side in the acute LBP group. The significant decrease of CSA in the MF muscle was found at multiple levels on the painful side. Conclusion: The present findings show that there is selective ipsilateral atrophy of paraspinal muscles, specific to the symptomatic side, in patients with acute and chronic LBP. The reduction of the muscle CSA and increased fatty infiltration occurred synchronously, and the extent of change is significantly greater in chronic LBP in the ES muscle. Atrophy of the MF muscle appears to be at multiple levels but side specific in relation to symptoms in patients with chronic LBP, and the decreased muscle CSA may occur prior to fatty infiltration. [ABSTRACT FROM AUTHOR]