O8.6. IS JUMPING TO CONCLUSIONS BIAS ASSOCIATED WITH FREQUENT "JUMPING" TO SALIENCE-RELATED FUNCTIONAL BRAIN STATES?. (9th April 2019) - Miyata, Jun; Sasamoto, Akihiko; Ezaki, Takahiro; Masuda, Naoki; Mori, Yasuo; Isobe, Masanori; Aso, Toshihiko; Murai, Toshiya; Takahashi, Hidehiko
Abstract: Background: Delusion is a false belief with strong conviction and incorrigibility. It is well documented that people with delusions and schizophrenia show the jumping to conclusions (JTC) bias, which means patients need less evidence for judgment than healthy people. Recently, JTC bias is indicated to be associated with aberrant salience in schizophrenia (Speechley et al, 2010). However, its neural representation is unknown. In this study, we employed the beads task, which measures JTC, and resting-state functional magnetic resonance imaging (rsfMRI) to reveal the neural correlates of the association between JTC bias and aberrant salience in patients with schizophrenia. Methods: Forty-one patients with schizophrenia (SCZ) and 34 healthy controls (HC) were recruited. All participants performed the beads task in the following procedure: subjects were presented with two jars containing 80 blue / 20 yellow and 20 blue / 80 yellow beads, respectively. Beads were drawn from one of the jars repeatedly with replacement, and subjects were told to decide from which of the two jars the beads were drawn. The number of draws needed to decision (DTD) was used as the index of JTC bias. The rsfMRI data were acquired from all the subjects on a Siemens 3T scanner, preprocessed by independent component analysis (ICA)-based denoising (Aso et al, 2017), and analyzed by group ICA implemented in FSL. Nine independent components (ICs) were identified as the networks of interest (NOIs) based on previous literature: the anterior, posterior, and ventral default mode networks (DMNs), left and right central executive networks (CENs), salience network (SN), medial temporal lobe network (MTLN) and basal ganglia network (BGN). The time-courses of these 9 ICs were analyzed by the Energy Landscape Analysis (ELA: Watanabe et al, 2013; Ezaki et al, 2018). ELA utilized pairwise maximum entropy model and Boltzmann machine to calculate "energy" of brain activation patterns (states), and created an energy landscape, which represented frequency of brain states. Transition rate between low-energy, stable states and high-energy, unstable states was calculated, and the effect of DTD and diagnosis and interaction between them on the transition rate were tested, with age, gender, IQ and temporal signal-to-noise ratio of rsfMRI data as covariates. Results: Low-energy, stable states were characterized by activation and deactivation of almost all NOIs, while high-energy, unstable states were characterized by activation and deactivation of salience-related NOIs. A significant interaction was found between DTD and diagnosis (p<0.05, FWE), indicating smaller DTD was correlated with more frequent transition between low and high energy states in SCZ patients, while larger DTD was correlated with more frequent transition in HC subjects. Discussion: This study revealed dynamic neural correlates of JTC bias and its association with aberrant salience in schizophrenia. These findings elucidate the pathway how aberrant salience in schizophrenia lead to psychotic symptom such as delusion.