Characteristics of Oil-in-Oil Emulsions under AC Electric Fields.

Emulsions have been applied in a number of industries such as pharmaceutics, cosmetics, and food, which are also of great scientific interest. Although aqueous emulsions are commonly used in our daily life, oil-in-oil (o/o) emulsions also play an irreplaceable role in view of their unique physics and complementary applications. In this paper, we investigate typical behaviors of organic droplets surrounded by organic medium (o/o emulsions) with different functional groups controlled by the AC electric field. Droplet behaviors can be catalogued into five types: namely, "no effect", "movement", "deformation", "interface rupture", and "disorder". We identify the key dimensionless number Wee·Ca, combined with the channel geometry, for characterizing the typical behaviors in silicon oil/1,6-hexanediol diacrylate and mineral oil/1,6-hexanediol diacrylate emulsions. Unlike aqueous emulsion, the Maxwell-Wagner relaxation inhibits the electric effect and leads to an effective frequency, ranging from 0.5 to 3 kHz. The increasing viscosity of the droplet facilitates the escalation by promoting the shearing effect under the same flow conditions. Ethylene glycol droplets primarily show the efficient coalescence even at a low Wee·Ca, which is attributed to the attraction of free charges induced by the increasing conductivity. In 1,6-hexanediol diacrylate/silicon oil emulsion, the droplet tends to form a liquid film that expands into the entire channel due to the affinity of the droplet to the channel wall. A variety of elongated columns are observed to oscillate between the electrodes at high voltages. These findings can contribute to understanding the electrohydrodynamic physics in o/o emulsion and controlling droplet behaviors in a fast response, programmable, and high-throughput way. We expect that this droplet manipulation technology can be widely adopted in a broad range of chemical synthesis and biological and material science.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.

Identification of essential tremor based on resting-state functional connectivity.

Currently, machine-learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole-brain resting-state functional connectivity (RSFC) metrics combined with machine-learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET-related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine-learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello-thalamo-motor and non-motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine-learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.

Prediction of Ki-67 expression in gastrointestinal stromal tumors using radiomics of plain and multiphase contrast-enhanced CT.

OBJECTIVE: To study the value of radiomics models based on plain and multiphase contrast-enhanced CT to predict Ki-67 expression in gastrointestinal stromal tumors (GISTs). METHODS: A total of 215 patients with GISTs were retrospectively analyzed, including 150 patients in one hospital as the training set and 65 patients in another hospital as the external verification set. The tumor at the largest level of CT images was delineated as the region of interest (ROI). The maximum diameter of the ROI was defined as the tumor size. A total of 851 radiomics features were extracted from each ROI by 3D Slicer Radiomics. After dimensionality reduction, three machine learning classification algorithms including logistic regression (LR), random forest (RF), and support vector machine (SVM) were used for Ki-67 expression prediction. Using a multivariable logistic model, a nomogram was established to predict the expression of Ki-67 individually. RESULTS: Delong tests showed that the SVM models had the highest accuracy in the arterial phase (Z value 0.217-1.139) and venous phase (Z value 0.022-1.396). For the plain phase, LR and SVM models had the highest accuracy (Z value 0.874-1.824, 1.139-1.763). For the delayed phase, LR models had the highest accuracy (Z value 0.056-1.824). For the combined phase, RF models had the highest accuracy (Z value 0.232-1.978). There was no significant difference among the above models for KI-67 expression prediction (Z value 0.022-1.978). A nomogram was developed with a C-index of 0.913 (95% CI, 0.878 to 0.956). CONCLUSIONS: Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. CLINICAL RELEVANCE STATEMENT: CT radiomics could accurately predict the expression of Ki-67 in GIST, which has a great clinical value in reflecting the proliferative activity of tumor cells and helping determine whether a patient is suitable for adjuvant therapy with imatinib. KEY POINTS: • Radiomics of both plain and enhanced CT images could accurately predict the expression of Ki-67 in GIST. • For patients who were not suitable to use contrast agents, plain scan could be used as an alternative. • A radiomics nomogram was developed to allow personalized preoperative evaluation with high accuracy.

High-performance compact athermal panoramic annular lens design with separated radial optical power.

A panoramic annular lens can provide abundant spatial information in real time with its compact and scalable structure. To deal with extreme temperature environments and to improve the utilization of an image plane, we propose a high-performance compact athermal panoramic annular lens with separated radial optical power. By introducing multiple free-form surfaces, the system achieves a field of view of (40∘∼100∘)×360∘, an f-number of 2.2, and a total length of 40 mm. The modulation transfer function of the system's full fields of view is greater than 0.5 at 200 lp/mm, and the distortion is less than 2.5%. It can work stably in the range of -30∘C to 70°C. The proposed system is compact, athermal, and high-performing and has broad application prospects in the fields of visual positioning and biomedicine.

Accurate mechanical-optical theoretical model of cross-axis sensitivity of an interferometric micro-optomechanical accelerometer.

An interferometric micro-optomechanical accelerometer usually has ultrahigh sensitivity and accuracy. However, cross-axis interference inevitably degrades the performance, including its detection accuracy and output signal contrast. To accurately clarify the influence of cross-axis interference, a modified mechanical-optical theoretical model is established. The rotation of the proof mass and the detected light intensity are quantitatively investigated with a load of cross-axis acceleration. A simulation and experiment are performed to verify the correctness of the theoretical model when the cross-axis acceleration is from 0 to 0.175 g. The results demonstrate that this model has a more than fivefold accuracy increase compared with conventional theoretical models when the cross-axis acceleration is from 0.06 to 0.175 g. In addition, we provide a suppression method to diminish the rotation of the proof mass based on squeeze film air damping, which significantly suppresses the contrast reduction caused by cross-axis interference.

Extending the Validity of Squeeze Film Damping Models with Lower Aspect Ratios.

Squeeze film air damping is a significant factor in the design of MEMS devices owing to its great impact on the dynamic performance of vibrating structures. However, the traditional theoretical results of squeeze film air damping are derived from the Reynolds equation, wherein there exists a deviation from the true results, especially in low aspect ratios. While expensive efforts have been undertaken to prove that this deviation is caused by the neglect of pressure change across the film, a quantitative study has remained elusive. This paper focuses on the investigation of the finite size effect of squeeze film air damping and conducts numerical research using a set of simulations. A modified expression is extended to lower aspect ratio conditions from the original model of squeeze film air damping. The new quick-calculating formulas based on the simulation results reproduce the squeeze film air damping with a finite size effect accurately with a maximum error of less than 1% in the model without a border effect and 10.185% in the compact model with a border effect. The high consistency between the new formulas and simulation results shows that the finite size effect was adequately considered, which offers a previously unattainable precise damping design guide for MEMS devices.

Bandwidth Optimization of MEMS Accelerometers in Fluid Medium Environment.

There is a constraint between the dynamic range and the bandwidth of MEMS accelerometers. When the input acceleration is comparatively large, the squeeze film damping will increase dramatically with the increase in the oscillation amplitude, resulting in a decrease in bandwidth. Conventional models still lack a complete vibration response analysis in large amplitude ratios and cannot offer a suitable guide in the optimization of such devices. In this paper, the vibration response analysis of the sensing unit of an accelerometer in large amplitude ratios is first completed. Then, the optimal design of the sensing unit is proposed to solve the contradiction between the dynamic range and the bandwidth of the accelerometer. Finally, the results of the vibration experiment prove that the maximum bandwidth can be achieved with 0~10g external acceleration, which shows the effectiveness of the design guide. The new vibration analysis with the complete model of squeeze film damping is applicable to all sensitive structures based on vibration, not limited to the MEMS accelerometer studied in this thesis. The bandwidth optimal scheme also provides a strong reference for similar structures with large oscillation amplitude ratios.

Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control.

Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for further study. Here, we report the implementation of supervised machine learning (ML) for the chemical vapor deposition (CVD) synthesis of high-quality quasi-1D few-layered WTe2 NRs. Feature importance analysis indicates that H2 gas flow rate has a profound influence on the formation of WTe2, and the source ratio governs the sample morphology. Notably, the growth mechanism of 1T' few-layered WTe2 NRs is further proposed, which provides new insights for the growth of intriguing 2D and 1D tellurides and may inspire the growth strategies for other 1D nanostructures. Our findings suggest the effectiveness and capability of ML in guiding the synthesis of 1D nanostructures, opening up new opportunities for intelligent materials development.

Disorganized resting-state functional connectivity between the dorsal attention network and intrinsic networks in Parkinson's disease with freezing of gait.

Freezing of gait (FOG) is a common and complex manifestation of Parkinson's disease (PD) and is associated with impairment of attention. The purpose of this study was to evaluate the functional network connectivity (FNc) changes between the dorsal attention network (DAN) and the other seven intrinsic networks relevant to attention, visual-spatial, executive and motor functions in PD with or without FOG. Forty-three idiopathic PD patients (21 with FOG [FOG+] versus 22 without FOG [FOG-]) and 18 healthy controls (HC) were recruited in this study. The data-driven independent component analysis (ICA) method was used to extract and analyze the above-mentioned resting-state networks (RSNs). Compared with FOG-, FOG+ displayed decreased positive connectivity between the DAN and medial visual network (mVN) and sensory-motor network (SMN) and increased negative connectivity between the DAN and default mode network (DMN). The within-network connectivity in the SMN and visual networks were decreased, whereas the connectivity within DMN was increased significantly in FOG+. Correlation analysis showed that the clock drawing test (CDT) scores were positively correlated with the functional connectivity of mVN (r = 0.573, p = 0.008) and lateral visual network (lVN) (r = 0.510, p = 0.022), the Timed Up and Go Test (TUG) duration were negatively correlated with the connectivity of SMN (r = -0.629, p = 0.003), and the Frontal Assessment Battery (FAB) scores were negatively correlated with the connectivity of DMN in FOG+. Functional connectivity was changed in multiple intra-networks in patients with FOG. Inordinate inter-network connectivity between the DAN and other intrinsic networks may partly contribute to the mechanism of freezing.

Altered local and matrix functional connectivity in depressed essential tremor patients.

BACKGROUND: Depression in essential tremor (ET) has been constantly studied and reported, while the associated brain activity changes remain unclear. Recently, regional homogeneity (ReHo), a voxel-wise local functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging, has provided a promising way to observe spontaneous brain activity. METHODS: Local FC analyses were performed in forty-one depressed ET patients, 49 non-depressed ET patients and 43 healthy controls (HCs), and then matrix FC and clinical depression severity correlation analyses were further performed to reveal spontaneous neural activity changes in depressed ET patients. RESULTS: Compared with the non-depressed ET patients, the depressed ET patients showed decreased ReHo in the bilateral cerebellum lobules IX, and increased ReHo in the bilateral anterior cingulate cortices and middle prefrontal cortices. Twenty-five significant changes of ReHo clusters were observed in the depressed ET patients compared with the HCs, and matrix FC analysis further revealed that inter-ROI FC differences were also observed in the frontal-cerebellar-anterior cingulate cortex pathway. Correlation analyses showed that clinical depression severity was positively correlated with the inter-ROI FC values between the anterior cingulate cortex and bilateral middle prefrontal cortices and was negatively correlated with the inter-ROI FC values of the anterior cingulate cortex and bilateral cerebellum lobules IX. CONCLUSION: Our findings revealed local and inter-ROI FC differences in frontal-cerebellar-anterior cingulate cortex circuits in depressed ET patients, and among these regions, the cerebellum lobules IX, middle prefrontal cortices and anterior cingulate cortices could function as pathogenic structures underlying depression in ET patients.

Design and Modification of a High-Resolution Optical Interferometer Accelerometer.

The Micro-Opto-Electro-Mechanical Systems (MOEMS) accelerometer is a new type of accelerometer that combines the merits of optical measurement and Micro-Electro-Mechanical Systems (MEMS) to enable high precision, small volume, and anti-electromagnetism disturbance measurement of acceleration, which makes it a promising candidate for inertial navigation and seismic monitoring. This paper proposes a modified micro-grating-based accelerometer and introduces a new design method to characterize the grating interferometer. A MEMS sensor chip with high sensitivity was designed and fabricated, and the processing circuit was modified. The micro-grating interference measurement system was modeled, and the response sensitivity was analyzed. The accelerometer was then built and benchmarked with a commercial seismometer in detail. Compared to the previous prototype in the experiment, the results indicate that the noise floor has an ultra-low self-noise of 15 ng/Hz1/2.

A Fast Radio Map Construction Method Merging Self-Adaptive Local Linear Embedding (LLE) and Graph-Based Label Propagation in WLAN Fingerprint Localization Systems.

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.

Effects of thalamic infarction on the structural and functional connectivity of the ipsilesional primary somatosensory cortex.

OBJECTIVES: To identify regions causally influenced by thalamic stroke by measuring white matter integrity, cortical volume, and functional connectivity (FC) among patients with thalamic infarction (TI) and to determine the association between structural/functional alteration and somatosensory dysfunction. METHODS: Thirty-one cases with TI-induced somatosensory dysfunction and 32 healthy controls underwent magnetic resonance imaging scanning. We reconstructed the ipsilesional central thalamic radiation (CTR) and assessed its integrity using fractional anisotropy (FA), assessed S1 ipsilesional changes with cortical volume, and identified brain regions functionally connected to TI locations and regions without TI to examine the potential effects on somatosensory symptoms. RESULTS: Compared with controls, TI patients showed decreased FA (F = 17.626, p < 0.001) in the ipsilesional CTR. TI patients exhibited significantly decreased cortical volume in the ipsilesional top S1. Both affected CTR (r = 0.460, p = 0.012) and S1 volume (r = 0.375, p = 0.049) were positively correlated with somatosensory impairment in TI patients. In controls, the TI region was highly functionally connected to atrophic top S1 and less connected to the adjacent middle S1 region in FC mapping. However, T1 patients demonstrated significantly increased FC between the ipsilesional thalamus and middle S1 area, which was adjacent to the atrophic S1 region. CONCLUSIONS: TI induces remote changes in the S1, and this network of abnormality underlies the cause of the sensory deficits. However, our other finding that there is stronger connectivity in pathways adjacent to the damaged ones is likely responsible for at least some of the recovery of function. KEY POINTS: • TI led to secondary impairment in the CTR and cortical atrophy in the ipsilesional top of S1. • TI patients exhibited significantly higher functional connectivity with the ipsilateral middle S1 which was mainly located within the non-atrophic area of S1. • Our results provide neuroimaging markers for non-invasive treatment and predict somatosensory recovery.

Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures.

The widespread application of networks is providing a better platform for the development of society and technology. With the expansion of the scope of network applications, many issues need to be solved. Among them, the maximization of utility and the improvement of security have attracted much attention. Many existing attacks mean the network faces security challenges. The concept of trust should be considered to address these security issues. Meanwhile, the utility of the network, including efficiency, profit, welfare, etc., are concerns that should be maximized. Over the past decade, the concepts of game and trust have been introduced to various types of networks. However, there is a lack of research effort on several key points in distributed networks, which are critical to the information transmission of distributed networks, such as expelling malicious nodes quickly and accurately and finding equilibrium between energy assumption and high transmission rate. The purpose of this paper is to give a holistic overview of existing research on trust and game theory in networks. We analyzed that network utility can be maximized in terms of effectiveness, profits, and security. Moreover, a possible research agenda is proposed to promote the application and development of game theory and trust for improving security and maximizing utility.

Single Chip-Based Nano-Optomechanical Accelerometer Based on Subwavelength Grating Pair and Rotated Serpentine Springs.

Optical coupling between subwavelength grating pairs allows for the precise measurement of lateral or vertical displacement of grating elements and gives rise to different types of displacement and inertial sensors. In this paper, we demonstrate a design for a nano-optomechanical accelerometer based on a subwavelength grating pair that can be easily fabricated by a single Silicon-on-insulator (SOI) chip. The parameters of the subwavelength grating pair-based optical readout, including period, duty cycle, thickness of grating and metal film, and the distance of the air gap, were optimized by combining a genetic algorithm and rigorous coupled wavelength analysis (RCWA) to obtain the optimal sensitivity to the displacement of suspended grating element and the acceleration. A corresponding mechanical design was also completed to meet the highly sensitive acceleration measurement requirement while considering the mechanical cross-axis sensitivity, dynamic range, bandwidth, and fabrication feasibility. This device was verified by both RCWA and finite-different-time-domain methods, and a tolerance analysis was also completed to confirm that it is able to achieve the extremely high optical displacement sensitivity of 1.8%/nm, acceleration-displacement sensitivity of 1.56 nm/mg, and acceleration measurement sensitivity of more than 2.5%/mg, which is almost one order of magnitude higher than any reported counterparts. This work enables a single SOI-based high performance accelerometer, and provides a theoretical basis and fabrication guides for the design.

A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET.

Fog-based MANET (Mobile Ad hoc networks) is a novel paradigm of a mobile ad hoc network with the advantages of both mobility and fog computing. Meanwhile, as traditional routing protocol, ad hoc on-demand distance vector (AODV) routing protocol has been applied widely in fog-based MANET. Currently, how to improve the transmission performance and enhance security are the two major aspects in AODV's research field. However, the researches on joint energy efficiency and security seem to be seldom considered. In this paper, we propose a source anonymity-based lightweight secure AODV (SAL-SAODV) routing protocol to meet the above requirements. In SAL-SAODV protocol, source anonymous and secure transmitting schemes are proposed and applied. The scheme involves the following three parts: the source anonymity algorithm is employed to achieve the source node, without being tracked and located; the improved secure scheme based on the polynomial of CRC-4 is applied to substitute the RSA digital signature of SAODV and guarantee the data integrity, in addition to reducing the computation and energy consumption; the random delayed transmitting scheme (RDTM) is implemented to separate the check code and transmitted data, and achieve tamper-proof results. The simulation results show that the comprehensive performance of the proposed SAL-SAODV is a trade-off of the transmission performance, energy efficiency, and security, and better than AODV and SAODV.

Essential tremor is associated with disruption of functional connectivity in the ventral intermediate Nucleus--Motor Cortex--Cerebellum circuit.

The clinical benefits of targeting the ventral intermediate nucleus (VIM) for the treatment of tremors in essential tremor (ET) patients suggest that the VIM is a key hub in the network of tremor generation and propagation and that the VIM can be considered as a seed region to study the tremor network. However, little is known about the central tremor network in ET patients. Twenty-six ET patients and 26 matched healthy controls (HCs) were included in this study. After considering structural and head-motion factors and establishing the accuracy of our seed region, a VIM seed-based functional connectivity (FC) analysis of resting-state functional magnetic resonance imaging (RS-fMRI) data was performed to characterize the VIM FC network in ET patients. We found that ET patients and HCs shared a similar VIM FC network that was generally consistent with the VIM anatomical connectivity network inferred from normal nonhuman primates and healthy humans. Compared with HCs, ET patients displayed VIM-related FC changes, primarily within the VIM-motor cortex (MC)-cerebellum (CBLM) circuit, which included decreased FC in the CBLM and increased FC in the MC. Importantly, tremor severity correlated with these FC changes. These findings provide the first evidence that the pathological tremors observed in ET patients might be based on a physiologically pre-existing VIM - MC - CBLM network and that disruption of FC in this physiological network is associated with ET. Further, these findings demonstrate a potential approach for elucidating the neural network mechanisms underlying this disease.

Relationship between hippocampal subfield volumes and memory deficits in patients with thalamus infarction.

Clinical studies have shown that thalamus infarction (TI) affects memory function. The thalamic nucleus is directly or indirectly connected to the hippocampal system in animal models. However, this connection has not been investigated using structural magnetic resonance imaging (MRI) in humans. From the pathological perspective, TI patients may serve as valid models for revealing the interaction between the thalamus and hippocampus in memory function. In this study, we aim to assess different hippocampal subfield volumes in TI patients and control subjects using MRI and test their associations with memory function. A total of 37 TI patients (TI group), 38 matched healthy control subjects (HC group), and 22 control patients with other stroke location (SC group) underwent 3.0-T MRI scans and clinical memory examinations. Hippocampal subfield volumes were measured and compared by using FreeSurfer software. We examined the correlation between hippocampal subfield volumes and memory scores. Smaller ipsilesional presubiculum and subiculum volumes were observed, and former was related to graphics recall in both left and right TI patients. The left subiculum volume was correlated with short-delayed recall in left TI patients. The right presubiculum volume was correlated with short- and long-delayed recall in right TI patients. TI was found to result in hippocampal abnormality and memory deficits, and its neural mechanisms might be related with and interaction between the thalamus and hippocampus.

Association between abnormal default mode network activity and suicidality in depressed adolescents.

BACKGROUND: Suicide is the second leading cause of death among 15- to 29-year-olds in China, and 60 % of suicidal patients have a history of depression. Previous brain imaging studies have shown that depression and suicide may be associated with abnormal activity in default mode network (DMN) regions. However, no study has specifically investigated the relationship between DMN functional activity and suicidal behavior in depressed individuals. Therefore, in the present study, we directly investigated features of DMN brain activity in adolescent patients with histories of depression and attempted suicide. METHODS: A total of 35 sex- and age-matched suicidal depressed patients were compared with 18 non-suicidal depressed patients and 47 healthy controls. We explored functional activity changes in DMN regions that could be associated with suicidal behavior by comparing resting-state functional magnetic resonance imaging (rs-fMRI) signals using independent component analysis (ICA). Scores on six clinical scales that measure depression severity (Hamilton Depression Scale (HDRS) and Beck Depression Inventory (BDI)) and suicidal traits (Barratt Impulsiveness Scale (BIS-11), Suicide Attitude Questionnaire (SAQ), Beck Hopelessness Scale (BHS), and Scale for Suicide Ideation (SSI)) were compared in the three groups. RESULTS: Compared with the healthy controls, all of the evaluated depressed patients showed increased functional connectivity in select DMN regions. The suicidal patients showed increased connectivity in the left cerebellum and decreased connectivity in the right posterior cingulate cortex (PCC), whereas the non-suicidal depressed patients showed increased connectivity in the left superior frontal gyrus, left lingual gyrus and right precuneus and decreased connectivity in the left cerebellum. Compared to the non-suicidal patients, the suicidal patients showed increased connectivity in the left cerebellum and the left lingual gyrus and decreased connectivity in the right precuneus. No differences in the scores of any clinical scales were found between the suicidal and non-suicidal depressed patients. CONCLUSIONS: Collectively, our results highlight the importance of the DMN in the pathophysiology of depression and suggest that suicidal behavior in depressed adolescents may be related to abnormal functional connectivity in the DMN. In particular, abnormal connectivity in the PCC/precuneus and left cerebellum might be a predictor of suicidal behavior in depressed adolescent patients.