Radiology Report Generation (RRG) is designed to improve the quality of radiology reports, reduce the workload and subjective errors of radiologists, and facilitate clinical automation. Previous approaches have typically used encoder-decoder architectures, mostly by injecting additional auxiliary information (e.g., knowledge graphs etc.) into the model to improve model performance; few studies have explored cross-modal information interaction and memory utilization. Here, we present a boosted cross-modal information joint memory alignment (BcI-mA) Transformer to efficiently generate radiology report tasks. First, we design a cross-modal information boost module (BcI), which can effectively enhance the interaction between intra- and inter-frame vector features and can be dynamically and iteratively updated with the training process as auxiliary information. Second, a plug-and-play module, which consists of BcI and the memory-aligned (mA) attention mechanism, is designed and embedded into BcI-mA. The method flexibly injects auxiliary information extracted from BcI into mA and utilizes it for information alignment with historical memory. The experimental results show that BcI-mA achieves substantial improvements and outperforms the latest state-of-the-art methods in benchmarking two popular radiology reporting datasets. Further analysis demonstrated that our method is capable of generating radiology reports that contain the necessary medical anomaly information terms.

Objective: Functional abnormalitiesz in classic trigeminal neuralgia (CTN) have been confirmed in previous studies, implying an influential role of alterations in brain networks in exploring the pathogenesis and treatment strategies for CTN. Percutaneous balloon compression (PBC) is a successful therapy option for CTN patients. However, direct comparisons of pre- and post-treatment cohorts have been insufficient in existing research.Methods: In this article, 18 CTN patients and 27 healthy controls underwent resting-state fMRI to analyze differences in low-frequency fluctuations (ALFF), fractional ALFF (fALFF) among the CTN, PBC-treated and HC groups and further explored the alterations in large-scale brain networks. Finally, graph theory was adopted to quantify dynamic brain networks at global and nodal levels.Results: Compared with HCs, CTN exhibits abnormal spontaneous neural activity, characterized by increased ALFF in the bilateral cingulate gyrus (CG) and left superior frontal gyrus (L-SFG). Meanwhile, the sensorimotor network shows enhanced connectivity with the visual network (VN) and the default mode network (DMN). After PBC, widespread changes in brain function were observed, including increased ALFF in the CG and left posterior cingulate cortex. Additionally, network reorganization was reflected by a decrease in connectivity between the DMN and the VN. Furthermore, we observed a decrease in network efficiency and clustering coefficient, and an increase in feature path length relative to the pre-intervention state.Conclusion: These findings offer potential objective biomarkers for evaluating the clinical efficacy of PBC surgery and offer fresh perspectives on the neuropathological foundation and potential therapeutic targets for CTN, while also establishing important biological foundations for computational models of information transmission and network efficiency in social systems.

In this paper, a delay-angle information spoofing (DAIS) strategy is proposed for location-privacy enhancement. By shifting the location-relevant delays and angles without the aid of channel state information (CSI) at the transmitter, the eavesdropper is obfuscated by a physical location that is distinct from the true one. A precoder is designed to preserve location-privacy while the legitimate localizer can remove the obfuscation with the securely shared information. Then, a lower bound on the localization error is derived via the analysis of the geometric mismatch caused by DAIS, validating the enhanced location-privacy. The statistical hardness for the estimation of the shared information is also investigated to assess the robustness to the potential leakage of the designed precoder structure. Numerical comparisons show that the proposed DAIS scheme results in more than 15 dB performance degradation for the illegitimate localizer at high signal-to-noise ratios, which is comparable to a recently proposed CSI-free location-privacy enhancement strategy and is less sensitive to the precoder structure leakage than the prior approach.

In this paper, an augmented analysis of a delay-angle information spoofing (DAIS) is provided for location-privacy preservation, where the location-relevant delays and angles are artificially shifted to obfuscate the eavesdropper with an incorrect physical location. A simplified misspecified Cramer-Rao bound (MCRB) is derived, which clearly manifests that not only estimation error, but also the geometric mismatch introduced by DAIS can lead to a significant increase in localization error for an eavesdropper. Given an assumption of the orthogonality among wireless paths, the simplified MCRB can be further expressed as a function of delay-angle shifts in a closed-form, which enables the more straightforward optimization of these design parameters for location-privacy enhancement. Numerical results are provided, validating the theoretical analysis and showing that the root-mean-square error for eavesdropper's localization can be more than 150 m with the ontimized delay-angle shifts for DATS.

Major depressive disorder (MDD) which is a widespread disorder worldwide cause disruption in some brain functions and thus leads to brain network changes. An increasing number of clinical and cognitive neuroscience studies have used broadband EEG microstate method to assess the electrical activity of large-scale cortical networks; however, the topographic frequency patterns of the different EEG microstate categories in MDD patients are not clear. In this study, EEG microstate frequency spectra were analyzed using 5-min resting-state electroencephalography (EEG) data from 55 subjects (including 27 MDD patients and 28 healthy controls) with variational empirical mode decomposition in Hilbert-Huang transform. The results showed that microstate D and the other microstates (A, C, and E) display opposite patterns. Most specifically, in the beta band, the marginal spectral energies of microstates A, C, and E were increased in MDD patients compared to HCs, while the energy of microstate D was decreased. Meanwhile, we observed that the marginal spectral energies of microstates A and C in the beta band were positively correlated with the severity of depressive symptoms, suggesting that alterations in the beta band energy can serve as an important reference for disease progression. These results confirm the abnormalities of beta-band energy in MDD patient and provide a new perspective for exploring the abnormalities of psychomotor function in patients with MDD.

Background: Despite potential neural mechanism of depression being the object of a thriving field, the research of the alterations in brain functional connections is not entirely clear. In this context, we used source-level effective connectivity and microstate analysis to study resting-state brain activity in depression.Methods: Resting-state electroencephalogram (EEG) data from 17 depressive subjects and 19 controls were included. We applied multivariate autoregressive models combined independent component analysis (MVARICA) and generalized partial directed coherence (GPDC) to analyze the brain functional system (BFS) alterations induced by depression. Further, microstate characterized the spatial organization and temporal dynamics of large-scale cortical activities was used for depression disease to understand brain network dynamics. Results: Compared with controls, depression had enhanced information flow from frontal to parietal in alpha band. Especially, the frontal and parietal lobes respectively was correspond to dominant hub in abnormally weaker and stronger causal pathways in patients with depression. In addition, microstate analysis revealed that mean duration, occurrence rate, time coverage of microstate class D were significantly lower in patients compared to controls. Meanwhile, patients preferred bilateral transitions between C and D compared with that in controls. Microstate D may be associated with the frontoparietal dorsal attention network, the findings reflects switching and reorientation of attention to relevant information occur more frequently for depressed patients.Conclusions: Patients exhibited clear effective network alterations compared to controls. Notably, EEG microstate analysis might provide useful biomarkers to understand the deviant functions of large-scale cortical activities in clinical researches of patients with depression.

In this paper, an artificial noise-aided strategy is presented for location-privacy preservation. A novel framework for the reduction of location-privacy leakage is introduced, where structured artificial noise is designed to degrade the structure of the illegitimate devices’ channel, without the aid of channel state information at the transmitter. Then, based on the location-privacy enhancement framework, a transmit beamformer is proposed to efficiently inject the structured artificial noise. Furthermore, the securely shared information is characterized to enable the legitimate devices to localize accurately. Numerical results show a 9dB degradation of illegitimate devices’ localization accuracy is achieved, and validate the efficacy of structured artificial noise versus unstructured Gaussian noise.

In this paper, a communication and control codesign framework is presented based on survival time, i.e., the time that a closed-loop wireless control system can continue without an anticipated message. The goal is to ensure the stability of wireless control systems with minimal resource usage. A novel interface between the controller and the scheduler is proposed, where the key communication and control parameters are analyzed for co-design, and jointly optimized. The proposed co-design framework leverages link adaptation for the communications system and sampling period adaptation for the closed-loop control system to preserve more resources. Our numerical example on closed-loop velocity control demonstrates a pronounced reduction of resources needed for control stability in contrast to the separate design paradigm that requires ultrahigh link reliability. An additional 52% reduction in resource utilization is achieved by further adapting the key parameters when the system is in survival mode.

It was observed that accuracy of the Subject-Dependent emotion recognition model was much higher than that of the Subject-Independent model in the field of electroencephalogram (EEG) based affective computing. This phenomenon is mainly caused by the individual difference of EEG, which is the key issue to be solved for the application of emotion recognition. In this work, 14 subjects from the SEED were selected for individual difference analysis. Through individual aggregation features evaluation, sample space visualization, and correlation analysis, we proposed four quantification indicators to analyze individual difference phenomenon. Finally, we presented the Personal-Zscore (PZ) feature processing method, and it was found that the data set processed with PZ method could represent emotion better than the original data set, and the conventional model with the PZ method was more robust. The accuracies of emotion recognition models trained with PZ processing have been improved to some extent, which showed that the PZ method could effectively eliminate the individual aggregation of feature space and improve the emotional representation ability of data sets. Hence, our findings may provide a new insight into the foundation for universal implementation of EEG-based application, and the Personal-Zscore feature processing method is of great significance for the development of effective emotion recognition system.

Emotional conflict control is impaired in major depression disorders (MDDs) and affects decision-making with further consequent social interactions dysfunction. However, neural correlates of conflict monitoring processes being modulated by different affective distractor stimuli are not clear in MDDs. In this article, we investigated abnormal neural basis of conflict monitoring processes in MDD patients by applying dynamic causal modeling (DCM) technique on electroencephalography (EEG). The results indicated that MDD patients showed lower N2 amplitudes regardless of stimulus conditions, and reduced activation within ACC region for incongruent stimuli, relative to healthy controls. Especially, MDDs had more negative N2 amplitudes to happy incongruent trials than happy congruent trials. Source localization analyses revealed that MDD patients had significantly enhanced left inferior temporal gyrus (ITG) activation, which is involved in written words processing. Further DCM analysis provided abnormal neural correlates through greater backward connections (fusiform→ITG, amygdala→ITG) on happy incongruent trials than happy congruent trials in MDD group. These findings indicate that only sad words induce significantly greater interference effects to positive target faces in MDD patients, which may be associated with ITG activity dysfunction. The findings may share new insights into the neural mechanisms of emotional conflict processing in MDDs.

Major depressive disorder (MDD) may be driven by dysfunction in intrinsic dynamic properties of the brain, and EEG microstate is a promising method for analyzing brain dynamics. However, the alterations in EEG microstate is still not entirely clear, and its ability for MDDs detection is worth probing. Moreover, the mechanism behind the neural networks contributing to microstates remains poorly understood in MDDs. Therefore, we applied microstate analysis and Topographic Electrophysiological State Source-imaging (TESS) on EEG data of 27 MDDs and 28 healthy controls (HCs). Compared to HCs, MDDs had apparent increase in microstate C and decrease in microstate D. Furthermore, TESS results showed that the underlying network of microstate C in MDDs overlapped with the anterior cingulate cortex and left insula gyrus, whereas main source of microstate D was in the orbital part of inferior frontal gyrus. The reduced transition probability from C to D in MDDs may reveal an imbalance between the networks of microstates. The microstate parameters as features reached good performance in identifying MDD (89.09% accuracy, 92.86% sensitivity, 85.19% specificity), indicating their potential as biomarkers of depression pathology. Collectively, these results highlight alteration of brain activity patterns and provide new insights into abnormal EEG dynamics in MDDs.

OBJECTIVE: Depression is accompanied by abnormalities in large-scale functional brain networks. This paper combined static and dynamic methods to analyze the abnormal topology and changes of functional connectivity network (FCN) of depression. METHODS: We collected resting-state EEG recordings from 27 depressed subjects and 28 normal subjects, then obtained 68 regions of interests (ROIs) by source localization. We took ROIs as the nodes and correlations as the edges to build FCNs and analyzed static network based on graph theory. We used a sliding window method followed by k-means clustering, states analyses and trend analysis of network metrics over time to study dynamic connectivity. RESULTS: The clustering coefficient (CC) and local efficiency in depression were increased, the characteristic path length and global efficiency were decreased, and local metrics had different manifestations in different resting state networks (RSNs); Depression had reduced connectivity in most RSNs, but increased connectivity in the default mode network, and there was a decoupling phenomenon between different RSNs; Depressed patients spent more time in sparsely connected states, their FCN's flexibility was less than normal subjects; The trend of CC over time was opposite between two groups. Most metrics in normal showed a relatively stronger correlation with time. SIGNIFICANCE: Our research may provide a deeper understanding of neurophysiological mechanisms of depression and new biomarkers for clinical diagnosis of depression.

Herein, an atomic norm based method for accurately estimating the location and orientation of a target from millimeter-wave multi-input-multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) signals is presented. A novel virtual channel matrix is introduced and an algorithm to ex-tract localization-relevant channel parameters from its atomic norm decomposition is designed. Then, based on the extended invariance principle, a weighted least squares problem is pro-posed to accurately recover the location and orientation using both line-of-sight and non-line-of-sight channel information. Numerical results highlight performance improvements over a prior method and the resultant performance nearly achieves the Cramér-Rao lower bound.

Drug addicts are characterized by difficulty neglecting monetary reward, but its underlying neural mechanisms remain unclear. The current study aimed to investigate the behavioral and electrophysiological signatures of abnormal attentional bias based on different amounts of reward in abstinent heroin addicts (AHAs). We used a modified attentional capture task while recording EEG in 18 AHAs and 18 age-, gander-, and education-matched healthy controls (HCs). We analyzed the attentional distribution of the relative positional changes in space of the target and reward-related stimulus. When targets integrated reward-related colors, participants were more responsive and deployed more attention to targets, especially those with high-value colors. When targets and reward-related distractors were spatially separated, high-value distractors captured the AHA's attention and slowed their responses. Moreover, AHAs had weaker attentional control than HCs, exhibiting an inability to suppress the attentional bias driven by high-value stimuli. Overall, these results demonstrated that AHAs was hypersensitive to task-irrelevant and previous reward-related stimuli, possibly due to damage to brain reward circuits caused by chronic heroin abuse. Our work provides novel behavioral and neurophysiological evidence that are closely associated with the maintenance and relapse of addiction.

In this paper, improved channel gain delay estimation strategies are investigated when practical pulse shapes with finite block length and transmission bandwidth are employed. Pilot-aided channel estimation with an improved atomic norm based approach is proposed to promote the low rank structure of the channel. All the channel parameters, i.e., delays, Doppler shifts and channel gains are recovered. Design choices which ensure unique estimates of channel parameters for root-raised-cosine pulse shapes are examined. Furthermore, a perturbation analysis is conducted. Finally, numerical results verify the theoretical analysis and show performance improvements over the previously proposed method.

In this paper, improved channel gain delay estimation strategies are investigated when practical pulse shapes with finite block length and transmission bandwidth are employed. Pilot-aided channel estimation with an augmented atomic norm based approach is proposed to promote the low rank structure of the time-varying narrowband leaked channel. All the channel parameters, i.e., delays, Doppler shifts, and channel gains are recovered. Design choices which ensure unique estimates of channel parameters for rectangular, Gaussian, and root-raised-cosine pulse shapes are examined in the noiseless case, respectively. Furthermore, a perturbation analysis is conducted to measure the impact of noise and further design choices for parameters are proposed to mitigate the effects of noise. Finally, numerical results verify the theoretical analysis and show performance improvements over the previously proposed method.

Electroencephalography (EEG) have been proved to be effective in the field of depression recognition, however, the application of EEG-based mild depression detection is still in its infancy. Our work mainly focused on mild depression recognition of college students, based on high-density 128-channel EEG recordings from 24 mild depression individuals and 24 normal subjects using facial expression as experimental materials. In order to prevent individual performance differences on the convolution kernel, and to integrate time information and spatial information instead of simply combining, we proposed a new deep learning model with multiple convolution kernels and a Long Short-Term Memory (LSTM) strategy based on convolution. Batch normalization has been widely used and proved to be effective in some research areas, for example computer vision. Our findings show that for EEG data, batch normalization will reduce the accuracy due to the special data characteristics of EEG. It was found that the proposed model achieved an accuracy of 83.47% with the 8-fold cross-validation, and Batch Normalization will reduce the accuracy because it eliminated the difference between depression and normal. Our findings cast a new light to recognize mild depression accurately and quickly, it could be used as auxiliary tools to diagnose and predict mild depression in the future.

Previous studies have shown that functional changes in depression are a context-specific rather than generalized across stimuli. In our study, with the aim of investigating the differences in the functional networks, electroencephalogram (EEG) data were collected from 27 mild depression (MD) and 27 normal controls (NC) using the Cue - Target paradigm. The exact low resolution electromagnetic tomography (eLORETA) method is applied to estimate the three-dimensional distribution of the current density of the brain source, and lagged coherence(LC), lagged phase synchronization(LPS), lagged linear connectivity(LLC), lagged nonlinear connectivity(LNC) are used to calculate the functional connections between pairs of regions of interest. In the four frequency bands of delta, theta, alpha and beta, the clustering coefficient (CC) and characteristic path length (CPL) were calculated and statistical analysis was performed. Our research found that the electrophysiological activity of MD in Brodmann area (BA) 7 was stronger than that of NC in all frequency bands. When the cue is color block and the prompt is inconsistent with the target, the CC and CPL of MD and NC were significantly different in the delta, theta, and beta frequency band. This result showed that the functional network change of MD was most obvious when cue is an arrow and the cue is inconsistent with the target. In this condition, the CC and CPL of MD are greater than that of NC, indicating that these network characteristics might be used as biological indicators to identify MD.