Dataset Information

Multimodal Assessment of Schizophrenia and Depression Utilizing Video, Acoustic, Locomotor, Electroencephalographic, and Heart Rate Technology: Protocol for an Observational Study.

ABSTRACT:

Background

Current standards of psychiatric assessment and diagnostic evaluation rely primarily on the clinical subjective interpretation of a patient's outward manifestations of their internal state. While psychometric tools can help to evaluate these behaviors more systematically, the tools still rely on the clinician's interpretation of what are frequently nuanced speech and behavior patterns. With advances in computing power, increased availability of clinical data, and improving resolution of recording and sensor hardware (including acoustic, video, accelerometer, infrared, and other modalities), researchers have begun to demonstrate the feasibility of cutting-edge technologies in aiding the assessment of psychiatric disorders.

Objective

We present a research protocol that utilizes facial expression, eye gaze, voice and speech, locomotor, heart rate, and electroencephalography monitoring to assess schizophrenia symptoms and to distinguish patients with schizophrenia from those with other psychiatric disorders and control subjects.

Methods

We plan to recruit three outpatient groups: (1) 50 patients with schizophrenia, (2) 50 patients with unipolar major depressive disorder, and (3) 50 individuals with no psychiatric history. Using an internally developed semistructured interview, psychometrically validated clinical outcome measures, and a multimodal sensing system utilizing video, acoustic, actigraphic, heart rate, and electroencephalographic sensors, we aim to evaluate the system's capacity in classifying subjects (schizophrenia, depression, or control), to evaluate the system's sensitivity to within-group symptom severity, and to determine if such a system can further classify variations in disorder subtypes.

Results

Data collection began in July 2020 and is expected to continue through December 2022.

Conclusions

If successful, this study will help advance current progress in developing state-of-the-art technology to aid clinical psychiatric assessment and treatment. If our findings suggest that these technologies are capable of resolving diagnoses and symptoms to the level of current psychometric testing and clinician judgment, we would be among the first to develop a system that can eventually be used by clinicians to more objectively diagnose and assess schizophrenia and depression with the possibility of less risk of bias. Such a tool has the potential to improve accessibility to care; to aid clinicians in objectively evaluating diagnoses, severity of symptoms, and treatment efficacy through time; and to reduce treatment-related morbidity.

International registered report identifier (irrid)

DERR1-10.2196/36417.

SUBMITTER: Cotes RO

PROVIDER: S-EPMC9330209 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:OBJECTIVE:Changes in heart rate (HR) and locomotor activity reflect changes in autonomic physiology, behavior, and mood. These systems may involve interrelated neural circuits that are altered in psychiatric illness, yet their interactions are poorly understood. We hypothesized interactions between HR and locomotor activity could be used to discriminate patients with schizophrenia from controls, and would be less able to discriminate non-psychiatric patients from controls. APPROACH:HR and locomotor activity were recorded via wearable patches in 16 patients with schizophrenia and 19 healthy controls. Measures of signal complexity and interactions were calculated over multiple time scales, including sample entropy, mutual information, and transfer entropy. A support vector machine was trained on these features to discriminate patients from controls. Additionally, time series were converted into a network with nodes comprised of HR and locomotor activity states, and edges representing state transitions. Graph properties were used as features. Leave-one-out cross validation was performed. To compare against non-psychiatric illness, the same approach was repeated in 41 patients with atrial fibrillation (AFib) and 53 controls. MAIN RESULTS:Network features enabled perfect discrimination of schizophrenia patients from controls with an areas under the receiver operating characteristic curve (AUC) of 1.00 for training and test data. Other bivariate measures of interaction achieved lower AUCs (train 0.98, test 0.96), and univariate measures of complexity achieved the lowest performance. Conversely, interaction features did not improve discrimination of AFib patients from controls beyond univariate approaches. SIGNIFICANCE:Interactions between HR and locomotor activity enabled perfect discrimination of subjects with schizophrenia from controls, but these features were less performant in a non-psychiatric illness. This is the first quantitative evaluation of interactions between physiology and behavior in patients with psychiatric illness.

Project description:BACKGROUND:Information technology and video gaming have potential advantages in the treatment of schizophrenia. However, information regarding the habits and attitudes related to internet use and video gaming in people with schizophrenia is limited. OBJECTIVE:The aim of this study was to explore the habits and attitudes regarding video gaming and information technology usage and their associated factors in people with schizophrenia in Hong Kong. METHODS:In this cross-sectional survey, service users with schizophrenia were recruited from 6 halfway hostels and 7 integrated centers for mental wellness in Hong Kong. A 79-item self-report questionnaire was utilized to explore the habits of internet use and video gaming in these people with schizophrenia. The attitude toward video gaming was assessed using the Gaming Attitudes, Motivations, and Experiences Scales. Of the 148 individuals in a convenience sample who were invited to participate in this study, 110 willingly participated (a response rate of 74.3%). The data were analyzed using descriptive statistics, a two-tailed independent t test, Pearson correlation, and principal analysis with 3 methods of rotation (varimax, equimax, and promax). RESULTS:Most participants (100/110, 90.9%) had access to the internet and half of them (54/110, 49.1%) used the internet daily mostly to watch videos (66/110, 60.0%) or read news or books, etc (42/110, 38.2%). One-third of the participants (36/110, 32.7%) used the internet to play web-based games, and most of them (88/110, 80.0%) had played a video game in the past year. The most favorable gaming platforms were cellular phones (43/88, 49%) followed by computers (19/88, 22%) and arcade cabinets (6/88, 7%). The most favorable game genre was action games (34/145, 23.4%). Those who had a bachelor's degree or higher scored lower in social interaction than those with a lower education level (P=.03). Those who played video games daily scored higher in the category of story than those who did not play daily (t86=2.03, P=.05). The most popular gaming category was autonomy and the least popular categories were violent catharsis and violent reward. Two motives, "social playing" and "evasive playing," were formed to describe the characteristics of playing video games. CONCLUSIONS:Our data showed a high internet utilization rate among people with schizophrenia in Hong Kong. Only a few of them used the internet to search for health-related information. Our study also exemplified the unique habits of gaming among the participants. Health care professionals could utilize video games to engage people with schizophrenia and promote coping with stress and provide social skills training to such people with schizophrenia. Identification of the gaming attitudes can contribute to the development of serious games for the schizophrenic population. Further investigation is vital for the promotion of mental health through web-based platforms.

Project description:IntroductionSpinal anesthesia is utilized as an alternative to general anesthesia in infants for some surgeries. After spinal anesthesia, infants often become less conscious without administration of sedative medications. The aim of this study was to assess electroencephalographic (EEG) correlates after spinal anesthesia in a cohort of infants.Patients and methodsThis pilot study included 12 infants who underwent spinal anesthesia. Unprocessed electroencephalography was recorded. The electroencephalogram was interpreted by four neurologists. Processed analyses compared electroencephalogram changes 30 min after spinal anesthesia to baseline.ResultsFollowing spinal anesthesia, all 12 infants became sedated. Electroencephalography in all 12 demonstrated Stage 2 sleep with the appearance of sleep spindles (12-14 Hz) in the frontal and central leads in 8/12 (67%) of subjects. The median time to onset of sleep spindles was 24.7 interquartile range (21.2, 29.9) min. The duration of sleep spindles was 25.1 interquartile range (5.8, 99.8) min. Voltage attenuation and background slowing were the most common initial changes. Compared to baseline, the electroencephalogram 30 min after spinal anesthesia showed significantly increased absolute delta power (p = 0.02) and gamma power (p < 0.0001); decreases in beta (p = 0.0006) and higher beta (p < 0.0001) were also observed. The Fast Fourier Transform power ratio difference for delta/beta was increased (p = 0.03). Increased coherence was noted in the delta (p = 0.02) and theta (p = 0.04) bandwidths.DiscussionSpinal anesthesia in infants is associated with increased electroencephalographic slow wave activity and decreased beta activity compared to the awake state, with appearance of sleep spindles suggestive of normal sleep. The etiology and significance of the observed voltage attenuation and background slowing remains unclear.ConclusionsThe EEG signature of infant spinal anesthesia is distinct from that seen with general anesthesia and is consistent with normal sleep. Further investigation is required to better understand the etiology of these findings. Our preliminary findings contribute to the understanding of the brain effects of spinal anesthesia in early development.