Multisensory integration deficits in Schizophrenia and Autism evidenced in behaviour but not event related potentials

The process of integrating information from different sensory channels, known as multisensory integration (MSI) was assessed in two disorders, Autism Spectrum Disorder (ASD) and Schizophrenia (SCZ). 32 healthy controls (HC), 35 SCZ patients, and 23 ASD patients performed an audiovisual (AV) synchronous target detection task while reaction time (RT) and scalp recorded electrophysiological (EEG) activity were measured. MSI in the AV condition resulted in faster and less variable RTs compared to the unimodal conditions. Using our novel bootstrap method, MSI gain was observed in 78 % of HC, 26 % of ASD, and 48 % of SCZ patients. At the neural level, MSI in the AV condition resulted in larger amplitude of sensory evoked responses and cognitive P3 response compared to the corresponding unimodal conditions. These neural effects of MSI were not related to the behavioural MSI gain identified at the individual level and could not explain the deficits in behavioural MSI of patient groups. In conclusion, a robust MSI gain deficit in RT was observed in both patient groups that was not reflected in early perceptual and cognitive electro-cortical responses, suggesting that behavioural MSI deficits in ASD and SCZ may arise at late processing stages such as response selection.

Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders

Introduction

Monitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders.

Methods

We continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months. These included 5-min measures of total motor activity from an accelerometer (TMA), average Heart Rate (HRA) and heart rate variability (HRV) from a plethysmography-based sensor, walking activity (WA) measured as number of total steps per day and sleep/wake ratio (SWR). A self-reporting questionnaire (IPAQ) assessed weekly physical activity. After pooling phenotype data, their monthly mean and variance was correlated within each patient with psychopathology scores (PANSS) assessed monthly.

Results

Our results indicate that increased HRA during wakefulness and sleep correlated with increases in positive psychopathology. Besides, decreased HRV and increase in its monthly variance correlated with increases in negative psychopathology. Self-reported physical activity did not correlate with changes in psychopathology. These effects were independent from demographic and clinical variables as well as changes in antipsychotic medication dose.

Discussion

Our findings suggest that distinct digital phenotypes derived passively from a smartwatch can predict variations in positive and negative dimensions of psychopathology of patients with psychotic disorders, over time, providing ground evidence for their potential clinical use.

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures

Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.

Brain-Heart Link in Schizophrenia: Cognitive Inhibitory Control Deficit in Patients Is Specifically Related to Parasympathetic Dysregulation

BACKGROUND

This study examined the connection between two prominent deficits in schizophrenia: the deficit in parasympathetic regulation and the deficit in cognitive inhibitory control, within the framework of the Neurovisceral Integration Model (NIM).

STUDY DESIGN

Thirty healthy controls and 30 patients with schizophrenia performed the internationally standardized antisaccade protocol while their electrocardiographic data were recorded. The interaction between the group, the cognitive inhibitory control as measured with error rate (ER) in the antisaccade task and parasympathetic activity as measured with the High Frequency power component of Heart Rate Variability (HF-HRV) was tested.

STUDY RESULTS

Findings confirmed that decreased HF-HRV was specifically related to increased ER in patients with schizophrenia. In contrast, patient deficits in other oculomotor function measures such as reaction time and reaction time variability related to volitional movement control and cognitive stability respectively were not linked to the deficit in parasympathetic regulation.

CONCLUSIONS

Our study validates the theory behind NIM proposing that cognitive inhibition has common physiological substrate with parasympathetic regulation. Future research could test this brain-heart link in other mental disorders especially those with a prominent deficit in inhibitory cognitive function.

Shared variance of oculomotor phenotypes in a large sample of healthy young men

This study used canonical correlation analysis to investigate patterns of shared variance between parameters measured in seven different occulomotor function tasks, namely the visually guided saccade task, the antisaccade task, the closed-loop smooth-pursuit task, the open-loop smooth-pursuit task, and three active visual fixation tasks. These tasks were performed by 2130 young army recruits. Only a small percentage (1-10%) of shared variance existed between sets of parameters for all oculomotor function tasks measured. The most correlated tasks were the visually guided saccade and the antisaccade. The first common factor correlated with speed of performance between these tasks (latency), while the second and third correlated with accuracy of performance. Better performance in active visual fixation tasks correlated with better performance accuracy (lower error rate) and increased speed (lower latency) in the antisaccade and saccade tasks as well as better performance in the closed-loop smooth-pursuit task (increase in gain and decrease in the rate of unwanted saccades during pursuit). Better performance in the closed-loop smooth-pursuit task (increased gain and decreased number of unwanted saccades) also correlated with increased accuracy and increased speed of performing saccades and antisaccades. Finally, the open-loop fixation task had no correlation with all other oculomotor tasks except for a very weak negative correlation with the closed-loop pursuit task where better performance (increased gain) in one correlated with worse performance (decreased gain) in the other. The results of this analysis showed that a small percentage of variance is shared among different oculomotor function tasks. The structure of this shared variance could be used to derive common oculomotor function indices to study their relation to genetic and other sources of inter-subject variation.

Increased intra-subject reaction time variability in the volitional control of movement in schizophrenia

Increased Reaction Time (RT) studies intra-subject variability is an emerging and consistent finding in RT studies of schizophrenia. A group of 23 patients suffering from DSM-IV schizophrenia and a group of 23 age-matched control subjects performed two RT tasks requiring basic sensorimotor processing and engaging two different motor systems: the Finger Lift Reaction Time task and the Voluntary Saccade Reaction Time task. The Ex-Gaussian model was applied to the RT distributions measuring the mean (mu), and standard deviation (sigma) of a Gaussian component thought to reflect sensorimotor processing and an exponential component (tau), thought to reflect an intermediate decision process. In both tasks, a significantly larger RT intra-subject variability effectively dissociated patients from controls. RT intra-subject variability in the two tasks was highly correlated only for patients. Both sigma and tau were significantly higher in the patient group with tau being the best predictor of schizophrenia. Furthermore, only in the patient group were sigma and tau highly correlated between the two tasks. The results reflect a deficit in information processing that may not be confined to decision processes related to the frontal cortex; rather, they may indicate dysfunction in distributed neural networks modulating adaptive regulation of performance.

Larger variability of saccadic reaction times in schizophrenia patients

Slower mean reaction time (RT), known as psychomotor slowing, is well documented in patients with schizophrenia. Fewer studies have shown increased variability of RT in these patients suggesting a basic difference in the distribution of RT. In this study median RT and its variability were measured for visually guided saccades performed by 53 patients and 1089 control subjects. Then average cumulative RT distributions were derived for each group and the RT distribution for each group was modeled using a decision signal rising linearly to a threshold signaling the beginning of the visually guided saccade. There was a small increase in the median RT for patients while their RTs were much more variable from trial to trial leading to a difference in the average RT distribution of the patient group. The model application led to the conclusion that this difference in the distribution of RT for patients could be attributed to a basic difference in information processing leading to the decision to move the eyes to the visually presented target. This information-processing difference could be the result of a difference in the build-up of neuronal activity involved in the generation of visually guided saccades in the frontal cortex.