Schizophrenia Bulletin最新文献

Background: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders.

Hypothesis: Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD).

Study design: From a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored.

Study results: The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01).

Conclusions: ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.

Background: Since the late 1990s, there has been a worldwide surge of scientific interest in the pre-psychotic phase, resulting in the introduction of several clinical tools for early detection. The predictive accuracy of these tools has been limited, motivating the need for methodological and perspectival improvements. The EASE manual supports systematic assessment of anomalous self-experience, and proposes an overall model of understanding how most psychotic experiences may be initially generated on the basis of a unifying, fundamental, pre-reflective distortion of subjectivity.

Study design: The EASE is time-consuming, so in order to spread the use of this essential perspective of psychosis risk we selected prototypical and frequent phenomena from the EASE, combining them into SQuEASE-11. To investigate this instrument for clinical relevance, basic psychometric properties, factor structure, and relationships with gold standard instruments and the full EASE, it was administered as an interview in the STEP intervention trial (Melbourne, Australia), with 328 clinical high-risk for psychosis (CHR-P) patients.

Study results: The SQuEASE-11 had moderate internal consistency and revealed two correlated factors. Significant relationships were observed between the SQuEASE-11 and the widely used and validated instruments CAARMS, BPRS, SANS, MADRS, DACOBS, and SOFAS. The correlation with the full EASE was very strong.

Conclusions: These 11 items do not necessarily relate specifically to ipseity disturbance, but the SQuEASE-11 seems to be a clinically relevant and brief supplementary first-line interview in CHR-P subjects. It may give a qualified indication of the need for a complete EASE interview, and it may also, importantly, inform treatment planning.

Background and hypothesis: Schizophrenia is associated with a decreased pursuit of risky rewards during uncertain-risk decision-making. However, putative mechanisms subserving this disadvantageous risky reward pursuit, such as contributions of cognition and relevant traits, remain poorly understood.

Study design: Participants (30 schizophrenia/schizoaffective disorder [SZ]; 30 comparison participants [CP]) completed the Balloon Analogue Risk Task (BART). Computational modeling captured subprocesses of uncertain-risk decision-making: Risk Propensity, Prior Belief of Success, Learning Rate, and Behavioral Consistency. IQ, self-reported risk-specific processes (ie, Perceived Risks and Expected Benefit of Risks), and non-risk-specific traits (ie, defeatist beliefs; hedonic tone) were examined for relationships with Risk Propensity to determine what contributed to differences in risky reward pursuit.

Study results: On the BART, the SZ group exhibited lower Risk Propensity, higher Prior Beliefs of Success, and comparable Learning Rates. Furthermore, Risk Propensity was positively associated with IQ across groups. Linear models predicting Risk Propensity revealed 2 interactions: 1 between group and Perceived Risk, and 1 between IQ and Perceived Risk. Specifically, in both the SZ group and individuals with below median IQ, lower Perceived Risks was related to lower Risk Propensity. Thus, lower perception of financial risks was associated with a less advantageous pursuit of uncertain-risk rewards.

Conclusions: Findings suggest consistently decreased risk-taking on the BART in SZ may reflect risk imperception, the failure to accurately perceive and leverage relevant information to guide the advantageous pursuit of risky rewards. Additionally, our results highlight the importance of cognition in uncertain-risk decision-making.

Background and hypothesis: Abnormal psychomotor behavior is a core schizophrenia symptom. However, assessment of motor abnormalities with expert rating scales is challenging. The Positive and Negative Syndrome Scale (PANSS) includes 3 items broadly related to hypokinetic motor behavior. Here, we tested whether a sum score of the PANSS items mannerisms and posturing (G5), motor retardation (G7), and disturbance of volition (G13) corresponds to expert ratings, potentially qualifying as a proxy-marker of motor abnormalities.

Study design: Combining baseline datasets (n = 196) of 2 clinical trials (OCoPS-P, BrAGG-SoS), we correlated PANSS motor score (PANSSmot) and 5 motor rating scales. In addition, we tested whether the cutoff set at ≥3 on each PANSS motor item, ie, "mild" on G05, G07, and G13 (in total ≥9 on PANSSmot) would differentiate the patients into groups with high vs low scores in motor scales. We further sought for replication in an independent trial (RESIS, n = 102), tested the longitudinal stability using week 3 data of OCoPS-P (n = 75), and evaluated the validity of PANSSmot with instrumental measures of physical activity (n = 113).

Study results: PANSSmot correlated with all motor scales (Spearman-Rho-range 0.19-0.52, all P ≤ .007). Furthermore, the cutoff set at ≥3 on each PANSS motor item was able to distinguish patients with high vs low motor scores in all motor scales except using Abnormal Involuntary Movement Scale (Mann-Whitney-U-Tests: all U ≥ 580, P ≤ .017).

Conclusions: Our findings suggest that PANSSmot could be a proxy measure for hypokinetic motor abnormalities. This might help to combine large datasets from clinical trials to explore whether some interventions may hold promise to alleviate hypokinetic motor abnormalities in psychosis.

Anti-leucine-rich glioma-inactivated 1 (LGI1) antibody-associated encephalitis is a rare but clinically significant form of autoimmune encephalitis, predominantly affecting middle-aged men. Its heterogeneous clinical presentation often leads to misdiagnosis, commonly as other neurological or psychiatric disorders. This report details the case of a 46-year-old male who initially presented with depressive symptoms, personality changes, and visual hallucinations. Over time, his condition progressed to include memory impairment, disorganized behavior, and seizures. Initially misdiagnosed with schizophrenia, the correct diagnosis of LGI1 antibody-associated encephalitis was eventually established through positive serum and cerebrospinal fluid (CSF) tests for LGI1 antibodies. Neuroimaging findings revealed characteristic bilateral temporal lobe lesions. The patient demonstrated marked improvement following treatment with methylprednisolone and intravenous immunoglobulin, ultimately achieving significant recovery. This case highlights the critical importance of comprehensive antibody testing and neuroimaging in patients presenting with nonspecific psychiatric and neurological symptoms to prevent misdiagnosis and delays in appropriate treatment. The article also reviews the pathogenesis, clinical manifestations, diagnostic approaches, and therapeutic strategies for LGI1 antibody-associated encephalitis, aiming to enhance clinical awareness and optimize patient outcomes.

Background and hypothesis: Altered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors.

Study design: We employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations.

Study results: Compared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049).

Conclusions: Our findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits.

Background: Schizophrenia is conceptualized as a brain connectome disorder that can emerge as early as late childhood and adolescence. However, the underlying neurodevelopmental basis remains unclear. Recent interest has grown in children and adolescent patients who experience symptom onset during critical brain development periods. Inspired by advanced methodological theories and large patient cohorts, Chinese researchers have made significant original contributions to understanding altered brain connectome development in early-onset schizophrenia (EOS).

Study design: We conducted a search of PubMed and Web of Science for studies on brain connectomes in schizophrenia and neurodevelopment. In this selective review, we first address the latest theories of brain structural and functional development. Subsequently, we synthesize Chinese findings regarding mechanisms of brain structural and functional abnormalities in EOS. Finally, we highlight several pivotal challenges and issues in this field.

Study results: Typical neurodevelopment follows a trajectory characterized by gray matter volume pruning, enhanced structural and functional connectivity, improved structural connectome efficiency, and differentiated modules in the functional connectome during late childhood and adolescence. Conversely, EOS deviates with excessive gray matter volume decline, cortical thinning, reduced information processing efficiency in the structural brain network, and dysregulated maturation of the functional brain network. Additionally, common functional connectome disruptions of default mode regions were found in early- and adult-onset patients.

Conclusions: Chinese research on brain connectomes of EOS provides crucial evidence for understanding pathological mechanisms. Further studies, utilizing standardized analyses based on large-sample multicenter datasets, have the potential to offer objective markers for early intervention and disease treatment.