Multimodal Assessment of Schizophrenia Symptom Severity From Linguistic, Acoustic and Visual Cues.

Assessing the condition of every schizophrenia patient correctly normally requires lengthy and frequent interviews with professionally trained doctors. To alleviate the time and manual burden on those mental health professionals, this paper proposes a multimodal assessment model that predicts the severity level of each symptom defined in Scale for the Assessment of Thought, Language, and Communication (TLC) and Positive and Negative Syndrome Scale (PANSS) based on the patient's linguistic, acoustic, and visual behavior. The proposed deep-learning model consists of a multimodal fusion framework and four unimodal transformer-based backbone networks. The second-stage pre-training is introduced to make each off-the-shelf pre-trained model learn the pattern of schizophrenia data more effectively. It learns to extract the desired features from the view of its modality. Next, the pre-trained parameters are frozen, and the light-weight trainable unimodal modules are inserted and fine-tuned to keep the number of parameters low while maintaining the superb performance simultaneously. Finally, the four adapted unimodal modules are fused into a final multimodal assessment model through the proposed multimodal fusion framework. For the purpose of validation, we train and evaluate the proposed model on schizophrenia patients recruited from National Taiwan University Hospital, whose performance achieves 0.534/0.685 in MAE/MSE, outperforming the related works in the literature. Through the experimental results and ablation studies, as well as the comparison with other related multimodal assessment works, our approach not only demonstrates the superiority of our performance but also the effectiveness of our approach to extract and integrate information from multiple modalities.

Mental Status Detection for Schizophrenia Patients via Deep Visual Perception.

Schizophrenia is a mental disorder that will progressively change a person's mental state and cause serious social problems. Symptoms of schizophrenia are highly correlated to emotional status, especially depression. We are thus motivated to design a mental status detection system for schizophrenia patients in order to provide an assessment tool for mental health professionals. Our system consists of two phases, including model learning and status detection. For the learning phase, we propose a multi-task learning framework to infer the patient's mental state, including emotion and depression severity. Unlike previous studies inferring emotional status mainly by facial analysis, in the learning phase, we adopted a Cross-Modality Graph Convolutional Network (CMGCN) to effectively integrate visual features from different modalities, including the face and context. We also designed task-aware objective functions to realize better model convergence for multi-task learning, i.e., emotion recognition and depression estimation. Further, we followed the correlation between depression and emotion to design the Emotion Passer module, to transfer the prior knowledge on emotion to the depression model. For the detection phase, we drew on characteristics of schizophrenia to detect the mental status. In the experiments, we performed a series of experiments on several benchmark datasets, and the results show that the proposed learning framework boosts state-of-the-art (SOTA) methods significantly. In addition, we take a trial on schizophrenia patients, and our system can achieve 69.52 in mAP in a real situation.

Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques.

Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients' conversations. Researches have shown that assessments of thought disorder are crucial for tracking the clinical patients' conditions and early detection of clinical high-risks. Detecting such symptoms require a trained clinician's expertise, which is prohibitive due to cost and the high patient-to-clinician ratio. In this paper, we propose a machine learning method using Transformer-based model to help automate the assessment of the severity of the thought disorder of schizophrenia. The proposed model uses both textual and acoustic speech between occupational therapists or psychiatric nurses and schizophrenia patients to predict the level of their thought disorder. Experimental results show that the proposed model has the ability to closely predict the results of assessments for Schizophrenia patients base on the extracted semantic, syntactic and acoustic features. Thus, we believe our model can be a helpful tool to doctors when they are assessing schizophrenia patients.

Exploring Age-Related Changes in Resting State Functional Connectivity of the Amygdala: From Young to Middle Adulthood.

Functional connectivities of the amygdala support emotional and cognitive processing. Life-span development of resting-state functional connectivities (rsFC) of the amygdala may underlie age-related differences in emotion regulatory mechanisms. To date, age-related changes in amygdala rsFC have been reported through adolescence but not as thoroughly for adulthood. This study investigated age-related differences in amygdala rsFC in 132 young and middle-aged adults (19-55 years). Data processing followed published routines. Overall, amygdala showed positive rsFC with the temporal, sensorimotor and ventromedial prefrontal cortex (vmPFC), insula and lentiform nucleus, and negative rsFC with visual, frontoparietal, and posterior cingulate cortex and caudate head. Amygdala rsFC with the cerebellum was positively correlated with age, and rsFCs with the dorsal medial prefrontal cortex (dmPFC) and somatomotor cortex were negatively correlated with age, at voxel p < 0.001 in combination with cluster p < 0.05 FWE. These age-dependent changes in connectivity appeared to manifest to a greater extent in men than in women, although the sex difference was only evident for the cerebellum in a slope test of age regressions (p = 0.0053). Previous studies showed amygdala interaction with the anterior cingulate cortex (ACC) and vmPFC during emotion regulation. In region of interest analysis, amygdala rsFC with the ACC and vmPFC did not show age-related changes. These findings suggest that intrinsic connectivity of the amygdala evolved from young to middle adulthood in selective brain regions, and may inform future studies of age-related emotion regulation and maladaptive development of the amygdala circuits as an etiological marker of emotional disorders.

Depression and clinical progression in spinocerebellar ataxias.

BACKGROUND: Depression is a common comorbidity in spinocerebellar ataxias (SCAs) but its association with ataxia progression is not well understood. OBJECTIVES: To study the prevalence and influence of depressive symptoms in SCAs. METHODS: We studied 300 participants with SCA 1, 2, 3 and 6 from the Clinical Research Consortium for Spinocerebellar Ataxias (CRC-SCA) and repeatedly measured depressive symptoms by the 9-item Patient Health Questionnaire (PHQ-9) along with other clinical features including ataxia, functional status, and quality of life every 6 months for 2 years. We employed regression models to study the effects of depressive symptoms on clinical progression indexed by Scale for Assessment and Rating of Ataxia (SARA), Unified Huntington's Disease Rating Scale Part IV (UHDRS-IV) and EQ5D after adjusting for age, sex and pathological CAG repeats. RESULTS: Comorbid depression is common in SCAs (26%). Although the baseline prevalence of depression was similar among different SCA types, suicidal ideation was more frequently reported in SCA3 (65%). Depressive symptoms were associated with SARA scores but did not significantly progress over time within 2 years or deteriorate by increased numbers of pathological CAG repeats. The effects of depression on ataxia progression varied across different SCA types. Nevertheless, depression had consistently negative and significant impact on functional status and quality of life in all SCAs, even after accounting for ataxia progression. CONCLUSIONS: Depressive symptoms are not simply the consequence of motor disability in SCAs. Comorbid depression per se contributes to different health outcomes and deserves more attention when caring patients with SCAs.