Effect of subjective sleep quality on learning and memory in drug-free patients with schizophrenia

Bian, Yun; Lin, Chen; Ma, Botao; Han, Xiaole; Yue, Weihua; Yang, Fude; Wang, Zhixiong

doi:10.1016/j.psychres.2021.113849

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…The current study linked the extracted features from rs-fMRI od SZ, BP, and SAD with clinical scores using partial correlation analysis. Many studies have found a link between basic clinical parameters and psychotic traits (mainly focused on SZ disorder) (Hudgens-Haney et al, 2020; Bian et al, 2021; Chu et al, 2021; Huo et al, 2021; Pelizza et al, 2021; Targum et al, 2021; Teetharatkul et al, 2021). These studies are based on group-level data rather than individual patient data (Ozomaro et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Overlap across psychotic disorders: A functional network connectivity analysis

Dini

Bruni

Ramsøy

et al. 2022

Preprint

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Functional network connectivity has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not clear yet. This study focuses on finding overlap across these three psychotic disorders using dynamic FNC (dFNC) and compares it with static FNC (sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar Schizophrenia Network on Intermediate Phenotypes cohort. The data includes three groups of patients with schizophrenia (SZP, N=102), bipolar (BPP, N=102), and schizoaffective (SADP, N=102), their relatives SZR (N=102), BPR (N=102), SADR (N=102), and HC (N=118) groups. After estimating each individual's dFNC, we put them into three identical states. We estimated five different features, including occupancy rate (OCR), number of transitions, the total number of transitions, and the total distance traveled. Finally, the extracted features are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We found the OCR difference between SZP and SZR in state2, between BPP and HC in state1, and between SADP and HC in state2. Also, state2 OCR separates SZP from BPP, state3 OCR separates BPP from SZP and SADP. Moreover, the OCR and traveled distance feature extracted from SZ and BP could significantly predict PANSS Total and PANSS General scores. Finally, combined distance features of all disorders showed a significant relationship to PANSS Total and PANSS General scores.

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Section: Discussionmentioning

confidence: 99%

Overlap across psychotic disorders: A functional network connectivity analysis

Dini

Bruni

Ramsøy

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Evidence from a two-sample bidirectional Mendelian randomization study supports a causal relationship between sleep characteristics and schizophrenia [ 12 ]. Furthermore, sleep characteristics have been identified as potential therapeutic targets for patients with schizophrenia, with psychotic symptoms, neurocognitive decline, and suicidality showing associations with abnormal sleep architecture [ 13 – 15 ]. Dysregulation of neurotransmitters and pathways related to the biological clock genes may contribute to sleep architecture abnormalities [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Targeted metabolomics-based understanding of the sleep disturbances in drug-naïve patients with schizophrenia

Yan,

Li,

Zhang

et al. 2024

BMC Psychiatry

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Background Sleep disturbances are a common occurrence in patients with schizophrenia, yet the underlying pathogenesis remain poorly understood. Here, we performed a targeted metabolomics-based approach to explore the potential biological mechanisms contributing to sleep disturbances in schizophrenia. Methods Plasma samples from 59 drug-naïve patients with schizophrenia and 36 healthy controls were subjected to liquid chromatography-mass spectrometry (LC-MS) targeted metabolomics analysis, allowing for the quantification and profiling of 271 metabolites. Sleep quality and clinical symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Positive and Negative Symptom Scale (PANSS), respectively. Partial correlation analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) model were used to identify metabolites specifically associated with sleep disturbances in drug-naïve schizophrenia. Results 16 characteristic metabolites were observed significantly associated with sleep disturbances in drug-naïve patients with schizophrenia. Furthermore, the glycerophospholipid metabolism (Impact: 0.138, p<0.001), the butanoate metabolism (Impact: 0.032, p=0.008), and the sphingolipid metabolism (Impact: 0.270, p=0.104) were identified as metabolic pathways associated with sleep disturbances in drug-naïve patients with schizophrenia. Conclusions Our study identified 16 characteristic metabolites (mainly lipids) and 3 metabolic pathways related to sleep disturbances in drug-naïve schizophrenia. The detection of these distinct metabolites provide valuable insights into the underlying biological mechanisms associated with sleep disturbances in schizophrenia.

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