Automatic depression detection technology based on speech and text data is one of the current research hotspots. However, numerous current models may inadequately incorporate the individual characteristics important for improving detection precision. This leads to misjudgment when the model deals with a depressed patient whose behavior significantly differs from the majority of patients. Thus, the goal of this research is to extract individual characteristics from speech and text data among individuals and develop an efficient and stable personalized model for depression detection. We propose a personalized multimodal depression detection model (PMDDM). The proposed model generates personalized embeddings for each individual by utilizing both speech and text features, while learning depressive representations and integrating them with personalized embeddings for joint representations. Lastly, the model receives the joint representations to predict the individual's depression status. We evaluated the proposed model using two datasets related to depression detection, MODMA and MIDD. Compared to the best baseline methods, our method improved the recognition accuracy by 5.88% and 3.84% on the MODMA and MIDD datasets, respectively. The F1 scores increased by 4.55% and 3.45%, respectively. In the cross-dataset generalizability experiments, our method still outperformed the best baseline methods in F1 score and accuracy. Results suggest that the effective integration of personalized information can significantly improve the accuracy and generalizability of depression detection models. Our research provides novel insights into personalized depression detection.

Facial depression recognition is one of the current hot topics. Mainstream methods mainly focus on how to design deep models to effectively extract the difference in facial movements between depressed patients and healthy people. However, this difference changes when the stimulus source to which the subjects are exposed changes. This leads to the performance degradation in cross-stimulus situation and limits the practical application of this technology. We hold the opinion that why depressed patients show behavioral characteristics different from healthy people is that they have a specific stable pattern of responding to stimulus. Therefore, we incorporate stimuli into the modeling process for the first time and employ deep networks to learn stable representations between stimulus and response to achieve stable and effective modeling. Specifically, we propose a deep modeling framework to learn the stimulus-response pattern of the subject through the interaction relationship between the stimulus videos and the subject’s facial movements. We constructed a balanced depression dataset of 364 individuals with three different stimulus videos to verify the effectiveness of our method. The results show that our method achieves state-of-the-art and the best generalization performance in depression recognition. This stimulus-response pattern modeling provides a new perspective for recognizing depression.

Text content analysis for depression detection using machine learning techniques has become a prominent area of research. However, previous studies focused mainly on analyzing the textual content, neglecting the fundamental factors driving text generation. Consequently, existing models face the challenge of poor generalization to out-of-domain data as they struggle to capture the crucial features of depression. To address this, we propose a novel computational perspective of "stimulus-response patterns" that brings us closer to the essence of clinical diagnosis of depression. Adopting this computational perspective allows us to conceptually unify diverse datasets and generalize this perspective to common datasets in the field. We introduce the Stimulus-Response Patterns-aware Network (SRP-Net) as an exemplary approach within this computational perspective. To assess the performance of the SRP-Net, we constructed a multi-stimulus dataset and conducted experimental evaluations, demonstrating its exceptional cross-stimulus generalizability. Furthermore, we demonstrated the promising performance of SPR-Net in real medical scenarios and conducted an interpretability analysis of the stimulus-response patterns. Our research investigates the critical role of stimulus-response patterns in enhancing the generalizability of text-based depression detection models, which can potentially facilitate data-driven depression detection to approach the diagnostic accuracy of psychiatrists.

The accuracy and availability are the most critical and challenging problems for major depressive disorder (MDD) diagnosis. Limited receptive field and inaccurate visual interpretation always weaken the clinical application of deep learning-based depression recognition model. Thus, we propose a visually interpretable depression monitoring model termed Transformer and Convolutional with slot-attention (TC-slot) to assess depression from facial images. Specifically, this approach stands upon the intersection of convolution and transformer, combines self-attention mechanism and deep convolution, and uses a well-designed stem structure to explore the global and local relationships. Moreover, in TC-slot, a classifier built on slot-attention mechanism directly involved in the decision-making process further localizes salient regions of facial depression patterns and provides precise and meaningful explanations. The results indicate that the proposed approach effectively improves the classification and recognition performance compared with other state-of-the-art approaches, with guaranteed favorable visual interpretability, providing clinical insights into the assessment of the assessing depression.

Depression is a mental disorder characterized by persistent low mood that affects a person’s thoughts, behavior, feelings, and sense of well-being. According to the World Health Organization (WHO), depression will become the second major life-threatening illness in 2020. Electroencephalogram (EEG) signals, which reflect the working status of human brain, are regarded as the best physiological tool for depression detection. Previous studies used the Empirical Mode Decomposition (EMD) method, which can deal with the highly complex, nonlinear and non-stationary nature of EEG, to extract features from EEG signals. However, for some special data, the neighboring components extracted through EMD could certainly have sections of data carrying the same frequency at different time durations. Thus, the Intrinsic Mode Functions (IMFs) of the data could be linearly dependent and the features coefficients of expansion based on IMFs could not be extracted, which can make the pre-proposed EMD-based feature extraction method impractical. In order to solve this problem, an improved EMD applying Singular Value Decomposition (SVD)-based feature extraction method was proposed in this study, which can extract the features coefficients of expansion based on all IMFs as accurately as possible, ignoring potentially linear dependence of IMFs. Experiments were conducted on four EEG databases for detecting depression. The improved EMD-based feature extraction method can extract feature from all three channels (Fp1, Fpz, and Fp2) on the four EEG databases. The average classification results of the proposed method on the four EEG databases including depressed patients and healthy subjects reached 83.27, 85.19, 81.98 and 88.07 percent, respectively, which were comparable with the pre-proposed EMD-based feature extraction method.

Depression is a mental disorder with emotional and cognitive dysfunction. The main clinical characteristic of depression is significant and persistent low mood. As reported, depression is a leading cause of disability worldwide. Moreover, the rate of recognition and treatment for depression is low. Therefore, the detection and treatment of depression are urgent. Multichannel electroencephalogram (EEG) signals, which reflect the working status of the human brain, can be used to develop an objective and promising tool for augmenting the clinical effects in the diagnosis and detection of depression. However, when a large number of EEG channels are acquired, the information redundancy and computational complexity of the EEG signals increase; thus, effective channel selection algorithms are required not only for machine learning feasibility, but also for practicality in clinical depression detection. Consequently, we propose an optimal channel selection method for EEG-based depression detection via kernel-target alignment (KTA) to effectively resolve the abovementioned issues. In this method, we consider a modified version KTA that can measure the similarity between the kernel matrix for channel selection and the target matrix as an objective function and optimize the objective function by a proposed optimal channel selection strategy. Experimental results on two EEG datasets show that channel selection can effectively increase the classification performance and that even if we rely only on a small subset of channels, the results are still acceptable. The selected channels are in line with the expected latent cortical activity patterns in depression detection. Moreover, the experimental results demonstrate that our method outperforms the state-of-the-art channel selection approaches.

In recent years, automatic detection of depression from speech has attracted many researchers. One of the key points is finding discriminable patterns in voice between depressed patients and healthy people. For this goal, we employed the Hilbert-Huang transform (HHT) to implement time-frequency analysis. Speech signals were decomposed into different sub-band signals and further were transformed into energy-frequency features for analysis and detection of depression. In the experiment 124 participants' (68 females and 56 males) speech were recorded in three patterns: interview, reading, and picture description for data collection. The results showed that the energy distribution of intrinsic mode functions (IMFs) between depressed patients and healthy people was significantly different, and this difference mainly was found in a relatively high-frequency range (1kHz). This finding fitted the clinical observation of depressed patients' “energy loss”. Further, a speech-based depression classification model based on the above finding was built and validated on the dataset. The results showed classification accuracy was 75.5% and 71.2% for female and male, respectively and each specificity was 88.4% and 78.2% These results implied HHT-based energy-frequency feature is a promising indicator for automatic depression assessment.

Depression is a common mental disorder which is harmful to our family, economics and society. Many people cannot receive timely mental health services, and the diagnosis process is subjective. A primary way for reducing harm is finding an objective and effective depression detection approach. Speech and video are two promising behavior indicators for depression. In this paper, we proposed a speech and video bimodal fusion model based on time-frequency analysis and convolutional neural network for this goal. For the testing of the proposed method, a speech and video dataset of 292 participants were employed for cross-validation. Compared with the single modal classification results, the classification accuracy and generalization ability of this gender-independent model are further improved, which is helpful for the identification of depression.

Major depression disorder (MDD) has been proved to have difficulty in emotional conflict processing. The objective of this study is to investigate the different attention deployment patterns in emotional conflict processing between MDDs and Healthy Controls (HCs) using eye tracking data. A face-word Stroop task was used, and 49 MDDs and 50 healthy controls (HCs) were recruited in the experiment. Finally, our results indicated that MDDs demonstrated lower accuracy (ACC) compared with HCs during the process of emotional conflict. Moreover, we found attention bias in the process of attention maintenance but not vigilance, and it may be one of the possible reasons for different ability of emotional conflict processing between MDDs and HCs.

Depression is a mental disorder characterized by emotional and cognitive dysfunction, which appears a state of low mood and aversion to activity. Depression can affect a person's thoughts, behavior, feelings, and sense of well-being. Depression is projected to be the second major life-threatening illness in 2020 by World Health Organization (WHO). Thus, it is urgent to detect and treat depression. Electroencephalogram (EEG) signals, which objectively reflect the working status of the human brain, are considered as promising physiological tools for depression detection. Negatively biased processing of affective stimuli in depression has been proven. In order to detect depression more effectively, we proposed an affective auditory stimuli induced depression detection method from EEG signals. In this method, we applied negative, positive and neutral affective auditory stimuli with several frequency selected from the International Affective Digitized Sounds (IADS-2) to induce negative affective bias in patients with depression. We synchronously collected EEG signals with three electrodes located on the prefrontal lobe (Fpl, Fpz, and Fp2), then extracted efficacious features by Empirical Mode Decomposition (EMD) based feature extraction method to detect depression effectively. The results of the proposed method showed that high-frequency affective auditory stimuli were more effective in depression detection and the frequency of affective auditory stimuli was a crucial property, which can influence the effectiveness of affective auditory stimuli in depression detection.

Depression, influencing millions of people, has become a major disease in the past decade. However, the assessment methods of diagnosing depression almost exclusively rely on patient-reported or clinical judgments of symptom severity, which are associated with subjective biases and intensive labor. Some bio-signals such as EEG and eye movements are used for automatic detection but their accuracies are not accurate enough for the real application, further improvements are needed. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, generating data subsets by the content of the experiment, then using the majority vote of subsets to determine the subjects' label. The validation of the method is testified by two different experiments which included free viewing eye tracking and task-state EEG and these two experiments have 36, 40 subjects respectively. In these two experiments CBEM gains accuracies of 82.5% and 92.73% respectively. The results show that CBEM outperform traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and give an objective and quantitative evaluation of depression, which in the future could be used for the auxiliary diagnosis of depression.

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.