Schizophrenia is a psychiatric disorder with cognitive dysfunction as a core symptom along with positive and negative symptoms. Cognitive dysfunction in schizophrenia can be broadly classified into neurocognitive and social cognitive deficits, with these deficits significantly influencing social functioning. Therapeutic interventions aiming to enhance neurocognition and social cognition have been developed. In this review, we describe the characteristics of cognitive dysfunction in patients with schizophrenia, its relationship to social function, and intervention strategies. J. Med. Invest. 71 : 205-209, August, 2024

Schizophrenia is a mental illness that involves both genetic and environmental factors. Clozapine, an atypical antipsychotic, is a well-established therapy for treatment-resistant schizophrenia. In this study, we focused on a set of monozygotic twins with treatment-resistant schizophrenia in which one twin effectively responded to clozapine treatment and the other did not. Our previous study generated neurons from induced pluripotent stem (iPS) cells derived from these patients and compared the transcriptome profiles between mock- and clozapine-treated neurons. In this study, we performed genome-wide DNA methylation profiling to investigate the mechanisms underlying gene expression changes. First, we extracted the differentially methylated sites from each twin based on statistical analysis. Then, we combined the DNA methylation profiling with transcriptome profiling from our previous RNA-seq data. Among the genes with altered methylation and expression, we found the different proportions of the genes related to neuronal and synaptic functions between the clozapine responder and non-responder (35.7 and 6.7%, respectively). This trend was observed even when the basal differences between the responder and non-responder was excluded. These results suggest that effective clozapine action may correct the abnormalities of neuronal and synapse functions in schizophrenia via changes in methylation.

Genomic DNA methylation is involved in many diseases and is expected to be a specific biomarker for even the pre-symptomatic diagnosis of many diseases. Thus, a rapid and inexpensive detection method is required for disease diagnosis. We have previously reported that cytosine methylation in G-quadruplex (G4)-forming oligonucleotides develops different G4 topologies. In this study, we developed a method for detecting CpG methylation in G4-forming oligonucleotides based on the structural differences between methylated and unmethylated G4 DNAs. The differences in G4 topologies due to CpG methylation can be discriminated by G4 ligands. We performed a binding assay between methylated or unmethylated G4 DNAs and G4 ligands. The binding abilities of fluorescent G4 ligands to BCL-2, HRAS1, HRAS2, VEGF G4-forming sequences were examined by fluorescence-based microtiter plate assay. The differences in fluorescence intensities between methylated and unmethylated G4 DNAs were statistically significant. In addition to fluorescence detection, the binding of G4 ligand to DNA was detected by chemiluminescence. A significant difference was also detected in chemiluminescence intensity between methylated and unmethylated DNA. This is the first study on the detection of CpG methylation in G4 structures, focusing on structural changes using G4 ligands.

Recently, the use of digital technologies, such as avatars and virtual reality, has been increasingly explored to address university students’ mental health issues. However, there is limited research on the advantages and disadvantages of counselors using avatars in online video counseling. Herein, 25 university students were enrolled in a pilot online counseling session with a human counselor-controlled avatar, and asked about their emotional experiences and impressions of the avatar and to provide qualitative feedback on their communication experience. Positive emotions during the session were associated with impressions of the avatar’s intelligence and likeability. The anthropomorphism, animacy, likeability, and intelligent impressions of the avatar were interrelated, indicating that the avatar’s smile and the counselor’s expertise in empathy and approval may have contributed to these impressions. However, no associations were observed between participant experiences and their prior communication with avatars, or between participant experiences and their gender or the perceived gender of the avatar. Accordingly, recommendations for future practice and research are provided. Accumulating practical and empirical findings on the effectiveness of human-operated avatar counselors is crucial for addressing university students’ mental health issues.

Background/Objectives: Multimodal depression detection research has traditionally relied on early or hybrid fusion strategies without systematically analyzing how dynamic fusion mechanisms interact with modality-specific pretraining. Although gated and attention-based architectures are increasingly adopted, their behavior is rarely examined within a structured fusion taxonomy framework. Methods: In this study, we conduct a controlled taxonomy-level evaluation of multimodal fusion strategies in a Japanese PHQ-9-annotated depression dataset. We compare four fusion paradigms (concatenation, summation, gated fusion, and cross-attention) across three integration stages, crossed with modality-specific affective pretraining configurations for visual (CMU-MOSI/MOSEI), acoustic (JTES), and textual (WRIME) encoders, yielding 512 experimental conditions. Results: The results reveal strong position-dependent effects of fusion strategy. Cross-attention fusion at the audio integration stage achieved the highest mean AUC (0.774) and PR-AUC (0.606), with statistically significant superiority over gated and concatenation-based fusion (Kruskal–Wallis $H=86.28$, $p<0.001$). In contrast, fusion effects at the text stage were non-significant in AUC but significant in PR-AUC, highlighting metric-sensitive behavior under class imbalance. Pretraining effects were modality-specific: SigLIP initialization produced significant positive transfer $(\mathrm{\Delta }=+0.018, p<0.001)$, whereas audio pretraining on JTES resulted in negative transfer $(\mathrm{\Delta }=-0.014, p=0.004)$, suggesting domain mismatch effects. Gate analysis further revealed condition-dependent modality dominance, including cases of semantic–geometric reversal under joint auxiliary augmentation. Conclusions: Our findings suggest that multimodal depression detection systems should not be interpreted through static fusion categories alone. Instead, modality contribution appears to be associated with structured interaction effects between fusion strategy, integration position, and affective pretraining. This work provides a controlled empirical bridge between fusion taxonomy and dynamic modality weighting in clinical multimodal modeling.

Depression is a serious social issue, and early detection is crucial for improving mental health care. This study constructed a Japanese multimodal dataset for early depression detection, collecting text, audio, video, and heart rate data from 30-minute interviews with professional counselors. Psychological questionnaires were also administered before and after interviews. Feature extraction and correlation analysis with questionnaire results confirmed the dataset's reliability, aligning with existing psychological research. The dataset was then used to train DepMamba, a latest depression detection model, comparing three training methods. Fine-tuning the constructed dataset after pre-training on DAIC-WOZ significantly improved recall, enhancing depression detection performance. However, data limitations and label imbalances highlight the need for future dataset expansion and objective annotation. This research advances depression detection technology and supports the practical application of mental health care.