Reward biases spontaneous neural reactivation during sleep.

Sleep favors the reactivation and consolidation of newly acquired memories. Yet, how our brain selects the noteworthy information to be reprocessed during sleep remains largely unknown. From an evolutionary perspective, individuals must retain information that promotes survival, such as avoiding dangers, finding food, or obtaining praise or money. Here, we test whether neural representations of rewarded (compared to non-rewarded) events have priority for reactivation during sleep. Using functional MRI and a brain decoding approach, we show that patterns of brain activity observed during waking behavior spontaneously reemerge during slow-wave sleep. Critically, we report a privileged reactivation of neural patterns previously associated with a rewarded task (i.e., winning at a complex game). Moreover, during sleep, activity in task-related brain regions correlates with better subsequent memory performance. Our study uncovers a neural mechanism whereby rewarded life experiences are preferentially replayed and consolidated while we sleep.

Early detection of psychosis: helpful or stigmatizing experience? A qualitative study.

AIM: Despite the large scientific debate concerning potential stigmatizing effects of identifying an individual as being in an at-risk mental state (ARMS) for psychosis, studies investigating this topic from the subjective perspective of patients are rare. This study assesses whether ARMS individuals experience stigmatization and to what extent being informed about the ARMS is experienced as helpful or harmful. METHODS: Eleven ARMS individuals, currently participating in the follow-up assessments of the prospective Basel Früherkennung von Psychosen (FePsy; English: Early Detection of Psychosis) study, were interviewed in detail using a semistructured qualitative interview developed for this purpose. Data were analysed using Interpretative Phenomenological Analysis. RESULTS: Most individuals experiencing first symptoms reported sensing that there was 'something wrong with them' and felt in need of help. They were relieved that a specific term was assigned to their symptoms. The support received from the early detection centre was generally experienced as helpful. Many patients reported stigmatization and discrimination that appeared to be the result of altered behaviour and social withdrawal due to the prepsychotic symptoms they experienced prior to contact with the early detection clinic. CONCLUSIONS: The results suggest that early detection services help individuals cope with symptoms and potential stigmatization rather than enhancing or causing the latter. More emphasis should be put on the subjective experiences of those concerned when debating the advantages and disadvantages of early detection with regard to stigma. There was no evidence for increased perceived stigma and discrimination as a result of receiving information about the ARMS.

Alpha oscillations underlie working memory abnormalities in the psychosis high-risk state.

Working memory (WM) functioning, known to be modulated by neural oscillations, is impaired in schizophrenic psychoses. It remains unclear whether in the psychosis high-risk state, WM encoding is altered or whether patients are impaired at shielding their WM against distractors. We employed single-trial analyses of neurophysiological and behavioral data recorded during a WM paradigm, designed to include predictable distractors, on 18 patients with an at-risk mental state for psychosis (ARMS, 26.1±5.45 years) and 21 healthy controls (HCs, 25.5±3.95 years). Strong distractors were associated with reduced WM accuracy (p=0.036), but only ARMS patients required more processing time for strong distractors (p=0.002). Increased parieto-occipital alpha amplitude preceding distractor presentations was associated with enhanced accuracy only in HCs (p=0.009). During encoding, increased intertrial alpha phase locking values were associated with increased performance. Reduced shielding mechanisms against distractors in ARMS patients could lead to defective WM maintenance, which may result in significant confusion that may contribute to the formation of psychotic symptoms.

Correlations between self-rating and observer-rating of psychopathology in at-risk mental state and first-episode psychosis patients: influence of disease stage and gender.

AIM: Research findings on the correlations between self-rating and observer-rating of schizophrenic psychopathology are inconsistent and have rarely considered first-episode psychosis (FEP) and at-risk mental state (ARMS) for psychosis patients. This study investigates these correlations in ARMS and FEP patients and how they are moderated by disease stage and gender. METHODS: In the Basel Früherkennung von Psychosen (FePsy) study, positive and negative psychotic and affective symptoms were rated in 126 ARMS and 94 FEP patients using two observer- and three self-rating scales. The agreement between self-rating and observer-rating and the moderating influence of disease stage and gender was quantified using Pearson correlation and multiple regression models. RESULTS: Correlations between self- and observer-rated subscales covering the same symptom dimension were low and mostly non-significant except for one correlation of positive and one of negative symptoms. There was no moderating influence of disease stage and gender on the correlations between self-rating and observer-rating except for one higher association in positive symptoms in FEP compared to ARMS and in women compared to men. However, these significant interaction effects did not withstand correction for multiple testing. CONCLUSIONS: This study suggests that the agreement between self-rating and observer-rating in FEP and ARMS patients is rather low, similar across symptom dimensions, and only partially dependent on disease stage and gender. However, low correlations between self-rating and observer-rating do not necessarily indicate that these patients have difficulties reporting their symptoms. They could also have occurred because the scales did not exactly cover the same symptom dimensions.

Neural oscillations in antipsychotic-naïve patients with a first psychotic episode.

OBJECTIVES: In chronic schizophrenic psychoses, oscillatory abnormalities predominantly occur in prefrontal cortical regions and are associated with reduced communication across cortical areas. Nevertheless, it remains unclear whether similar alterations can be observed in patients with a first episode of psychosis (FEP), a state characterised by pathological features occurring in both late prodromal patients and initial phases of frank schizophrenic psychoses. METHODS: We assessed resting-state electroencephalographic data of 31 antipsychotic-naïve FEP patients and 29 healthy controls (HC). We investigated the three-dimensional (3D) current source density (CSD) distribution and lagged phase synchronisation (LPS) of oscillations across small-scale and large-scale brain networks. We additionally investigated LPS relationships with clinical symptoms using linear mixed-effects models. RESULTS: Compared to HC, FEP patients demonstrated abnormal CSD distributions in frontal areas of the brain; while decreased oscillations were found in the low frequencies, an increase was reported in the high frequencies (P < 0.01). Patients also exhibited deviant LPS in the high frequencies, whose dynamics changed over increasing 3D cortico-cortical distances and increasing psychotic symptoms. CONCLUSIONS: These results indicate that in addition to prefrontal cortical abnormalities, altered synchronised neural oscillations are also present, suggesting possible disruptions in cortico-cortical communications. These findings provide new insights into the pathophysiological mechanisms of emerging schizophrenic psychoses.

Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients.

OBJECTIVES: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients. METHODS: We assessed the individualised prediction of psychosis by detecting specific patterns of beta and gamma oscillations using machine-learning algorithms. Prediction models were trained and tested on 53 neuroleptic-naïve patients with a clinical high-risk for psychosis. Of these, 18 later transitioned to psychosis. All patients were followed up for at least 3 years. For an honest estimation of the generalisation capacity, the predictive performance of the models was assessed in unseen test cases using repeated nested cross-validation. RESULTS: Transition to psychosis could be predicted from current-source density (CSD; area under the curve [AUC] = 0.77), but not from lagged phase synchronicity data (LPS; AUC = 0.56). Combining both modalities did not improve the predictive accuracy (AUC = 0.78). The left superior temporal gyrus, the left inferior parietal lobule and the precuneus most strongly contributed to the prediction of psychosis. CONCLUSIONS: Our results suggest that CSD measurements extracted from clinical resting state EEG can help to improve the prediction of psychosis on a single-subject level.

Aberrant Current Source-Density and Lagged Phase Synchronization of Neural Oscillations as Markers for Emerging Psychosis.

BACKGROUND: Converging evidence indicates that neural oscillations coordinate activity across brain areas, a process which is seemingly perturbed in schizophrenia. In particular, beta (13-30 Hz) and gamma (30-50 Hz) oscillations were repeatedly found to be disturbed in schizophrenia and linked to clinical symptoms. However, it remains unknown whether abnormalities in current source density (CSD) and lagged phase synchronization of oscillations across distributed regions of the brain already occur in patients with an at-risk mental state (ARMS) for psychosis. METHODS: To further elucidate this issue, we assessed resting-state EEG data of 63 ARMS patients and 29 healthy controls (HC). Twenty-three ARMS patients later made a transition to psychosis (ARMS-T) and 40 did not (ARMS-NT). CSD and lagged phase synchronization of neural oscillations across brain areas were assessed using eLORETA and their relationships to neurocognitive deficits and clinical symptoms were analyzed using linear mixed-effects models. RESULTS: ARMS-T patients showed higher gamma activity in the medial prefrontal cortex compared to HC, which was associated with abstract reasoning abilities in ARMS-T. Furthermore, in ARMS-T patients lagged phase synchronization of beta oscillations decreased more over Euclidian distance compared to ARMS-NT and HC. Finally, this steep spatial decrease of phase synchronicity was most pronounced in ARMS-T patients with high positive and negative symptoms scores. CONCLUSIONS: These results indicate that patients who will later make the transition to psychosis are characterized by impairments in localized and synchronized neural oscillations providing new insights into the pathophysiological mechanisms of schizophrenic psychoses and may be used to improve the prediction of psychosis.