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Abnormal effort-based decision-making represents a potential mechanism underlying motivational deficits (amotivation) in psychotic disorders. Previous research identified effort allocation impairment in chronic schizophrenia and focused mostly on physical effort modality. No study has investigated cognitive effort allocation in first-episode psychosis (FEP).
Cognitive effort allocation was examined in 40 FEP patients and 44 demographically-matched healthy controls, using Cognitive Effort-Discounting (COGED) paradigm which quantified participants’ willingness to expend cognitive effort in terms of explicit, continuous discounting of monetary rewards based on parametrically-varied cognitive demands (levels N of N-back task). Relationship between reward-discounting and amotivation was investigated. Group differences in reward-magnitude and effort-cost sensitivity, and differential associations of these sensitivity indices with amotivation were explored.
Patients displayed significantly greater reward-discounting than controls. In particular, such discounting was most pronounced in patients with high levels of amotivation even when N-back performance and reward base amount were taken into consideration. Moreover, patients exhibited reduced reward-benefit sensitivity and effort-cost sensitivity relative to controls, and that decreased sensitivity to reward-benefit but not effort-cost was correlated with diminished motivation. Reward-discounting and sensitivity indices were generally unrelated to other symptom dimensions, antipsychotic dose and cognitive deficits.
This study provides the first evidence of cognitive effort-based decision-making impairment in FEP, and indicates that decreased effort expenditure is associated with amotivation. Our findings further suggest that abnormal effort allocation and amotivation might primarily be related to blunted reward valuation. Prospective research is required to clarify the utility of effort-based measures in predicting amotivation and functional outcome in FEP.
Better understanding of interplay among symptoms, cognition and functioning in first-episode psychosis (FEP) is crucial to promoting functional recovery. Network analysis is a promising data-driven approach to elucidating complex interactions among psychopathological variables in psychosis, but has not been applied in FEP.
This study employed network analysis to examine inter-relationships among a wide array of variables encompassing psychopathology, premorbid and onset characteristics, cognition, subjective quality-of-life and psychosocial functioning in 323 adult FEP patients in Hong Kong. Graphical Least Absolute Shrinkage and Selection Operator (LASSO) combined with extended Bayesian information criterion (BIC) model selection was used for network construction. Importance of individual nodes in a generated network was quantified by centrality analyses.
Our results showed that amotivation played the most central role and had the strongest associations with other variables in the network, as indexed by node strength. Amotivation and diminished expression displayed differential relationships with other nodes, supporting the validity of two-factor negative symptom structure. Psychosocial functioning was most strongly connected with amotivation and was weakly linked to several other variables. Within cognitive domain, digit span demonstrated the highest centrality and was connected with most of the other cognitive variables. Exploratory analysis revealed no significant gender differences in network structure and global strength.
Our results suggest the pivotal role of amotivation in psychopathology network of FEP and indicate its critical association with psychosocial functioning. Further research is required to verify the clinical significance of diminished motivation on functional outcome in the early course of psychotic illness.
The COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) project is a large international collaborative effort to analyze individual-level phenotype data from twins in multiple cohorts from different environments. The main objective is to study factors that modify genetic and environmental variation of height, body mass index (BMI, kg/m2) and size at birth, and additionally to address other research questions such as long-term consequences of birth size. The project started in 2013 and is open to all twin projects in the world having height and weight measures on twins with information on zygosity. Thus far, 54 twin projects from 24 countries have provided individual-level data. The CODATwins database includes 489,981 twin individuals (228,635 complete twin pairs). Since many twin cohorts have collected longitudinal data, there is a total of 1,049,785 height and weight observations. For many cohorts, we also have information on birth weight and length, own smoking behavior and own or parental education. We found that the heritability estimates of height and BMI systematically changed from infancy to old age. Remarkably, only minor differences in the heritability estimates were found across cultural–geographic regions, measurement time and birth cohort for height and BMI. In addition to genetic epidemiological studies, we looked at associations of height and BMI with education, birth weight and smoking status. Within-family analyses examined differences within same-sex and opposite-sex dizygotic twins in birth size and later development. The CODATwins project demonstrates the feasibility and value of international collaboration to address gene-by-exposure interactions that require large sample sizes and address the effects of different exposures across time, geographical regions and socioeconomic status.
Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.
We retrospectively analyzed 9 hospital outbreaks that occurred during 2011–2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.
Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.
Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.
Recovery of multidrug-resistant (MDR) Pseudomonas aeruginosa and Klebsiella pneumoniae from a cluster of patients in the medical intensive care unit (MICU) prompted an epidemiologic investigation for a common exposure.
Clinical and microbiologic data from MICU patients were retrospectively reviewed, MICU bronchoscopes underwent culturing and borescopy, and bronchoscope reprocessing procedures were reviewed. Bronchoscope and clinical MDR isolates epidemiologically linked to the cluster underwent molecular typing using pulsed-field gel electrophoresis (PFGE) followed by whole-genome sequencing.
Of the 33 case patients, 23 (70%) were exposed to a common bronchoscope (B1). Both MDR P. aeruginosa and K. pneumonia were recovered from the bronchoscope’s lumen, and borescopy revealed a luminal defect. Molecular testing demonstrated genetic relatedness among case patient and B1 isolates, providing strong evidence for horizontal bacterial transmission. MDR organism (MDRO) recovery in 19 patients was ultimately linked to B1 exposure, and 10 of 19 patients were classified as belonging to an MDRO pseudo-outbreak.
Surveillance of bronchoscope-derived clinical culture data was important for early detection of this outbreak, and whole-genome sequencing was important for the confirmation of findings. Visualization of bronchoscope lumens to confirm integrity should be a critical component of device reprocessing.