The brain patterns of emotional perception remain a pivotal research domain in affective neuroscience. Modeling the brain as a complex network has become a crucial approach to understanding its functions. However, traditional brain network research based on graph theory primarily focuses on dyadic interactions between brain regions, which cannot effectively characterize the information exchange process among multiple brain regions during emotional cognitive processes. To address these limitations, we shift our perspective from graph theory to the topological data analysis (TDA) of minimal 1-cycles. The 1 cycles or loops within a network represent the fundamental high order interactions in complex networks and serve as essential pathways for information transmission and integration among the distributed networks of brain regions. By focusing on cycle structures in affective brain networks, we propose a novel SMA-EL method based on the collaborative optimization of the Minimal 1-Cycle with Simplicial Maps Annotation (SMA-M1C) method and linear programming, which balances computational efficiency and method performance to reconstruct the optimal cycles in the brain network. This method is applied to the analysis of emotional brain networks in response to positive and negative emotions induced by naturalistic viewing. Comprehensive experiments demonstrate that the 1-cycle structures of the brain's functional patterns exhibit differences at both individual and group levels, aligning with prior research. Furthermore, the 1 cycles we proposed can serve as a biological marker for emotion recognition. These findings may provide new insights into the organization patterns of functional brain networks under diverse emotional states.

Image segmentation is a fundamental task in computer vision and computational social systems, with semantic segmentation aiming to assign each pixel to a corresponding label. Due to the inherent richness of categories and contextual information in images, image segmentation remains a challenging problem. Currently, semantic segmentation models based on the segment anything model have demonstrated promising results. However, they continue to encounter challenges related to training strategies and prompt information generation. To address these issues, we propose a straightforward and efficient design method for semantic segmentation based on a prompt-free model, named SAM-SS. First, we introduce the class prompt encoder, which generates category prompts for the mask decoder to extract category-specific semantic information. Second, we incorporate the deep fusion module to bridge the semantic gap for achieving robust representation. Additionally, we observe that fine-tuning the image encoder via low-rank adaptation often leads to suboptimal convergence. To mitigate this, we propose a learning rate modulation strategy to stabilize training and boost model performance. Finally, we validate our model’s performance on three publicly available datasets. Specifically, on the Cityscapes validation set for natural images, our model achieves a mean intersection over union (mIoU) of 85.28%. Moreover, our model demonstrates strong adaptability to remote sensing imagery, achieving mIoU scores of 54.46% on LoveDA and 80.8% on the ISPRS Potsdam validation sets. These results underscore its potential utility across diverse applications.

Depression is a common and serious mental disorder, characterized by persistent low mood, loss of interest, cognitive dysfunction, and physiological changes. Patients may experience symptoms such as sleep disturbances, changes in appetite, fatigue, and low self-esteem, with severe cases potentially leading to suicidal behavior. There are differences in emotional processing and attention allocation between patients with depression and healthy controls, eye movement characteristics such as fixation patterns, saccade amplitude, and attentional bias have been used as physiological signals for depression detection. Many researchers have developed depression recognition models based on ocular imaging. However, convolutional neural networks, which utilize local receptive fields, can only capture local features in ocular imaging. This paper proposes Multi-Scale Temporal-Frequency Attention Network (MTFNet), which innovatively integrates Multi-Scale time-frequency domain attention into the Video Swin Transformer. Through Multi-Scale Temporal-Frequency Attention Module (MTFAM), MTFNet learns the most important regions in eye movement images, enabling it to capture features more effectively from sequential data and gain a deeper understanding of the structure within eye movement images. Experimental results show that the proposed method achieves a high accuracy of 76.8% on a self-collected eye movement image dataset, outperforming most models. This work provides a novel approach to research on depression recognition based on eye movement images.

Depression remains a leading cause of suicide among college students, highlighting the need for effective and scalable screening methods. Internet usage behavior has shown strong potential for identifying depressive tendencies, but privacy concerns limit its practical use. In this study, we propose a privacy-conscious cross-scale adaptive transformer designed for irregular time series data derived from weakly private online behavior, such as application categories and usage patterns, while excluding content-sensitive or personally identifiable information. Our model incorporates an adaptive sampling strategy to unify temporal resolutions and uses a cross-scale attention mechanism to capture depression-related behavioral patterns. We compared several classic models for irregular time series data, and the proposed method outperformed them, offering a promising, non-intrusive approach for depression detection based on privacy-conscious online activity patterns.

The heterogeneity has been recognized as a large obstacle to the treatment of autism spectrum disorder (ASD). Recent studies have identified several subgroups of ASD that exhibited heterogeneous alterations in brain. However, most of them primarily depicted the pairwise similarity between individuals, relying solely on a single imaging modality. This leads to an underestimation of the complexity in inter-individual relationships and the rich information provided by multimodal images. To capture the high-order relationships among individuals, we utilized multi-task method to construct multilayer hypergraph based on brain structure and function. We then developed a novel co-optimized community detection algorithm, which jointly optimizes the modular structure across hypergraph layer, with the aim of categorizing subtypes of ASD by fusing multimodal information. By applying the proposed method on the Autism Brain Imaging Data Exchange repository data (n = 287/303, ASD/typical development [TD]), we identified two ASD subtypes with distinct alteration patterns in both brain structure and function. Distinct clinical manifestations in social and communication were observed between the two subtypes. Furthermore, subtyping significantly enhanced the diagnostic accuracy of ASD by over 10%. In addition, our method exhibited superior clustering performance that outperformed traditional community detection algorithms on graphs. Taken together, our study demonstrated the effectiveness of subtyping ASD through a multimodal multilayer hypergraph, highlighting its potential in elucidating the heterogeneity of autism and improving clinical diagnosis.

In contemporary society, the issue of psychological health has become increasingly prominent, characterized by the diversification, complexity, and universality of mental disorders. Cognitive Behavioral Therapy (CBT), currently the most influential and clinically effective psychological treatment method with no side effects, has limited coverage and poor quality in most countries. In recent years, researches on the recognition and intervention of emotional disorders using large language models (LLMs) have been validated, providing new possibilities for psychological assistance therapy. However, are large language models truly possible to conduct cognitive behavioral therapy? Many concerns have been raised by mental health experts regarding the use of LLMs for therapy. Seeking to answer this question, we collected real CBT corpus from online video websites, designed and conducted a targeted automatic evaluation framework involving three aspects, namely the evaluation of emotion tendency of generated text, structured dialogue pattern and proactive inquiry ability. Considering limited CBT-related texts in a general chat LLM’s training corpus, we evaluated the CBT ability of the LLM after integrating a CBT knowledge base to explore the influence of introducing additional knowledge. Four LLM variants with exceptional performance are evaluated, and the experimental result shows the great potential of LLMs in psychological counseling realm, especially after combining with other technological means.

Autism spectrum disorder (ASD) is accompanied by impaired social cognition and behavior. The expense of supporting patients with ASD turns into a significant problem for society. Parsing neurobiological subtypes is a crucial way for delineating the heterogeneity in autistic brains, with significant implications for improving ASD diagnosis and promoting the development of personalized intervention models. Nevertheless, a comprehensive understanding of the heterogeneity in cortical morphology of ASD is still lacking, and the question of whether neuroanatomical subtypes remain stable during cortical development remains unclear. Here, we used T1-weighted images of 515 male patients with ASD, including 216 autistic children (6–11 years), 187 adolescents (12–17 years), and 112 young adults (18–29 years), along with 595 age and gender-matched typically developing (TD) individuals. Cortical thickness (CT), surface area (SA), and volumes of cortical (CV) and subcortical (SV) regions were extracted. A single network layer was established by calculating the covariance of each feature across brain regions between participants, thereby constructing a multilayer intersubject covariance network. Applying a community detection algorithm to multilayer networks derived from different feature combinations, we observed that the network comprising CT and CV layers exhibited the most prominent modular organization, resulting in three subtypes of ASD for each of the three age groups. Subtypes within the corresponding age group significantly differed in terms of brain morphology and clinical scales. Furthermore, the subtypes of children with ASD underwent reorganization with development, transitioning from childhood to adolescence and adulthood, rather than consistently persist. Additionally, subtype categorization largely improved the diagnostic accuracy of ASD compared to diagnosing the entire ASD cohort. These findings demonstrated distinct neuroanatomical manifestations of ASD subtypes across various developmental periods, highlighting the significance of age-related subtyping in facilitating the etiology and diagnosis of ASD.

Brain anatomical age is an effective feature to assess the status of the brain, such as atypical development and aging. Although some deep learning models have been developed for estimating infant brain age, the performance of these models was unsatisfactory because few of them considered the developmental characteristics of brain anatomy during the perinatal period-the most rapid and complex developmental stage across the lifespan. The present study proposed an attention-based hemispheric relation inference network (HRINet) that takes advantage of the nature of brain structural lateralization during early development. This model captures the inter-hemispheric relationship using a graph attention mechanism and transmits lateralization information as features to describe the interactive development between bilateral hemispheres. The HRINet was used to estimate the brain age of 531 preterm and full-term neonates from the Developing Human Connectome Project (dHCP) database based on two metrics (mean curvature and sulcal depth) characterizing the folding morphology of the cortex. Our results showed that the HRINet outperformed other benchmark models in fitting the perinatal brain age, with mean absolute error of 0.53 and determination coefficient of 0.89. We also verified the generalizability of the HRINet on an extra independent dataset collected from the Gansu Provincial Maternity and Child-care Hospital. Furthermore, by applying the best-performing model to an independent dataset consisting of 47 scans of preterm infants at term-equivalent age, we showed that the predicted age was significantly lower than the chronological age, suggesting a delayed development of premature brains. Our results demonstrate the effectiveness and generalizability of the HRINet in estimating infant brain age, providing promising clinical applications for assessing neonatal brain maturity.

Functional magnetic resonance imaging (fMRI) plays a vital role in brain science as it measures and maps brain activity through the analysis of blood flow changes, offering valuable insights into cognitive functions and neural processes. However, due to the intricacy and dynamism of brain activity, conventional approaches failed to accurately predict the blood oxygen level-dependent (BOLD) time series in fMRI data. To tackle this issue, we proposed an end-to-end adversarial U-network (AUN) to verify the predictability of existing BOLD signals in primary cortex (i.e., primary visual, primary motor and primary sensory) and higher cortex(i.e., dorsolateral prefrontal and posterior cingulate). The model combined the U-network architecture and adversarial strategy to ensure that the predicted results capture both the intricate nonlinear details and the overall distribution characteristics. We performed the experiment using the Human Connectome Project (HCP) database. The results demonstrated the predictability of both primary and higher cortex, with primary cortex showing higher predictability. Additionally, the AUN performed better than other popular methods. We also found the improvement in dynamic functional connectivity (dFC) metrics through accurate prediction. The above results confirm the feasibility of predicting BOLD signals and their potential application in clinical settings.1

Diffusion magnetic resonance imaging (dMRI) is an important tool in characterizing tissue microstructure based on biophysical models, which are typically multi-compartmental models with mathematically complex and highly non-linear forms. Resolving microstructures from these models with conventional optimization techniques is prone to estimation errors and requires dense sampling in the q-space with a long scan time. Deep learning based approaches have been proposed to overcome these limitations. Motivated by the superior performance of the Transformer in feature extraction than the convolutional structure, in this work, we present a learning-based framework based on Transformer, namely, a Microstructure Estimation Transformer with Sparse Coding (METSC) for dMRI-based microstructural parameter estimation. To take advantage of the Transformer while addressing its limitation in large training data requirement, we explicitly introduce an inductive bias-model bias into the Transformer using a sparse coding technique to facilitate the training process. Thus, the METSC is composed with three stages, an embedding stage, a sparse representation stage, and a mapping stage. The embedding stage is a Transformer-based structure that encodes the signal in a high-level space to ensure the core voxel of a patch is represented effectively. In the sparse representation stage, a dictionary is constructed by solving a sparse reconstruction problem that unfolds the Iterative Hard Thresholding (IHT) process. The mapping stage is essentially a decoder that computes the microstructural parameters from the output of the second stage, based on the weighted sum of normalized dictionary coefficients where the weights are also learned. We tested our framework on two dMRI models with downsampled q-space data, including the intravoxel incoherent motion (IVIM) model and the neurite orientation dispersion and density imaging (NODDI) model. The proposed method achieved up to 11.25 folds of acceleration while retaining high fitting accuracy for NODDI fitting, reducing the mean squared error (MSE) up to 70% compared with the previous q-space learning approach. METSC outperformed the other state-of-the-art learning-based methods, including the model-free and model-based methods. The network also showed robustness against noise and generalizability across different datasets. The superior performance of METSC indicates its potential to improve dMRI acquisition and model fitting in clinical applications.

As a wireless basic unit, flexible antennas hold a wide range of applications in wearable electronics, soft robotics, and Internet of Things (IoT). However, most of the current flexible antennas are encapsulated by silicone elastomers with poor gas permeability, which severely hinders the evaporation of skin moisture and sweat. In addition, conventional rigid metals as high-frequency conductors are limited by poor elasticity and susceptibility to oxidation for on-skin application. Here, we developed a highly permeable and stretch-resistant flexible bowtie antenna that can capture changes in tensile strain and temperature. A low-impedance flexible carbon nanotube-silver (CNT-Ag) substrate was fabricated as the conductor of the antenna. By optimizing the multibeam bowed geometry and wrapping it in porous thermoplastic polyurethane (TPU) fibers, the final five-beam antenna was obtained and was able to withstand a relatively large tensile stress of 25.2 MPa, yet achieve a high vapor transmission rate of 48.2 mg cm−2 h−1. The antenna obtained an ideal impedance match at 2.28 GHz with doughnut-like radiation and a high radiation efficiency of over 85%. Furthermore, the antenna was successfully used to capture the strain in the wrist epidermis during bending and to detect thermal changes in the beaker of hot water, respectively. Finally, demonstrations of the antenna, such as permeability, radiation to the human body, and integrality in connection with flexible circuits, were carefully developed to reveal its feasibility in the real world. We expect this work to pave the way for the future establishment of epidermally flexible antennas for soft electronics.

Autism spectrum disorder (ASD) is accompanied with impaired social-emotional functioning, such as emotional regulation and recognition, communication, and related behavior. Study of the alternations of the brain networks in ASD may not only help us in understanding this disorder but also inform us the mechanisms of affective computing in the brain. Although morphological features have been used in the diagnosis of a variety of neurological and psychiatric disorders, these features did not show significant discriminative value in identifying patients with ASD, possibly due to the omission of the information related to the changes in structural similarities among cortical regions. In this study, structural images from 66 high-functioning adults with ASD and 66 matched typically-developing controls (TDC) were used to test the hypothesis of cortico-cortical relationships are abnormal in ASD. Seven morphological features of each of the 360 brain regions were extracted and elastic network was used to quantify the similarities between each target region and all other regions. The similarities were then used to construct multi-feature-based networks (MFN), which were then submitted to a support vector machine classifier to classify the individuals of the two groups. Results showed that the classifier with features of MFN significantly improved the accuracy of discriminating patients with ASD from TDCs (78.63 percent) compared to using morphological features only (<; 65 percent). The combination of MFN features with morphological features and other high-level MFN properties did not further enhance the classification performance. Our findings demonstrate that the variations in cortico-cortical similarities are important in the etiology of ASD and can be used as biomarkers in the diagnostic process.

Conventional hyper-network coefficients ignore the weighted hyper-edge information which could be vital in researching the specificity of brain disease. Functional hyper-networks for 64 healthy controls (HC) and 56 patients with major depressive disorder (MDD) were constructed using the least absolute shrinkage and selection operator (Lasso). Not only the classical topology metrics but also a novel hyper-edge weight (HEW) attribute were extracted as features to promote the functional-based auto-diagnosis accuracy of MDD. We compared the categorization performance of each hyper-network coefficient. A multi-feature ensemble model was applied to fuse different kinds of features. We obtained 82.15 % accuracy with the classical hyper-network clustering coefficient (HCC) and 84.08 % accuracy with the HEW attribute on the MDD dataset. The performance was further improved to 89.24% by combining all the properties of the hyper-networks. The multi-feature ensemble model combining different hyper-network coefficients provides new insights into the automatic diagnosis with diverse information of MDD.

Characterization of morphological organization pattern in individual brain has long been an open question. Recently, a novel single-subject network that built upon multiple morphological features (MFN) was introduced [1], which exhibited extraordinary power in diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study aims to characterize the cortico-cortical topological organization in MCI and AD cohorts via the MFN, and uncover the structural substrate that leads to the high classification accuracy. The MFNs were constructed for 165 normal controls (NCs), 221 patients with MCI, and 142 patients with AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Results from graph theoretical analysis showed `small-world' property and modular organization of the MFN in three groups; as well as increased local clustering and characteristic path length, and altered hub regions in patients with AD and MCI. More importantly, we found the primary atrophic regions were accompanied by increased number of in-flow connections; whereas, the connections that linked to the less atrophic regions were mostly out-flow connections. This phenomenon, we speculated, might indirectly reflect the trophic supporting effects in the brain. Our results demonstrated the basic organizational principles of the MFN and its changes in patients with AD and MCI, providing important implications of what makes the MFN powerful in auto-diagnosis of mental disorders.

Mild cognitive impairment (MCI) is a transition stage between normal aging and dementia. Brain network has been proven to occupy an important role in the study of differences in Alzheimer's disease (AD) and MCI. However, there is little knowledge about individual metabolic network abnormities which might be sensitive features in the prediction of MCI progression. In this paper, we constructed the individual metabolic network based on longitudinal Fluorodeoxyglucose positron emission tomography (FDG-PET) of 33 progress MCI (pMCI) patients and 46 stable MCI (sMCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI). Firstly, PET images of each time point are normalized with the Yakushev normalization procedure and registered to the Brainnetome Atlas (BNA) template. Then the combination of rough distance and precision distance is utilized for accurate evaluation of between-region dissimilarity and calculated the correlation coefficient of the network. Finally, correlative feature selected by Lasso shows a significant promotion in classification performance compare with the metabolic intensity, achieving an accuracy of 89.9% and area under the receiver operating characteristic curve of 0.892. What's more, the combination of multi time points also suggests a better classification result than single time point. This finding may predict disease course in individuals with mild cognitive impairment.

The rapid development of neuroimaging technology and brain network analysis methodologies have promoted the research of Alzheimer's disease (AD). Recently, studies on brain networks reported that AD patients showed abnormal connectivity alterations and disrupted coordinated organizations compared with normal controls (NC). However, much less knowledge is about the abnormalities of metabolic network at individual level, which might be the potential marker in promoting current AD diagnosis. In the present study, we constructed the individual metabolic network based on 18F-Fluro-Deoxyglucose Positron Emission Tomography (18F-FDG-PET) data by using cubes consisted with certain numbers of voxels. Network properties, connectivity strength and metabolic cost of cubes of 111 NCs and 111 AD patients were calculated to evaluate the performance and feasibility of the proposed network via machine learning approaches. Results showed that the features we extracted were well-performed in classification, with accuracy of 95.64% and area of 0.9915 under receiver operating characteristic curve, indicating the individual metabolic network and local metabolic information are potential powerful in AD diagnosis.