Functional brain network dysfunctions in subjects at high-risk for psychosis: A meta-analysis of resting-state functional connectivity

Fabro, Lorenzo Del; Schmidt, André; Fortea, Lydia; Delvecchio, Giuseppe; D’Agostino, Armando; Raduà, Joaquim; Borgwardt, Stefan; Brambilla, Paolo

doi:10.1016/j.neubiorev.2021.06.020

Cited by 39 publications

(27 citation statements)

References 84 publications

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“…This work denoted that the BACS score is rooted in a similar cognitive construct in all the patients and their relatives. Even though other groups of studies used brain imaging data of relatives and compared them with patients and HCs, they did not consider the dynamic nature of brain activity (Wang et al, 2015; Hager et al, 2017; Del Fabro et al, 2021). Johnsen et al reviewed fMRI studies which compared task-related brain alterations of SZR and BPR with SZP and BPP (Johnsen et al, 2020).…”

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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“…SZ has been associated with reduced functional connectivity across fronto-temporal networks supporting verbal encoding [ 9 ], hippocampal-prefrontal and prefrontal-thalamic networks during working-memory tasks [ 10 , 11 ], occipito-temporal networks supporting semantic processing [ 12 ], and dysfunctional integration between brain regions involved in reinforcement learning [ 13 , 14 ]. The recent meta-analysis of studies on resting-state functional connectivity among at-risk-of-psychosis individuals supports the hypothesis that large-scale network dysfunctions represent a core neural deficit underlying psychosis development [ 15 ]. For a long time, most of the studies discussed potential mechanisms of impairment in schizophrenia with respect to separate neural systems that could independently contribute to cognitive deficits in goal-directed behavior.…”

Section: Introductionmentioning

confidence: 92%

Confirmation Bias in the Course of Instructed Reinforcement Learning in Schizophrenia-Spectrum Disorders

et al. 2022

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A large body of research attributes learning deficits in schizophrenia (SZ) to the systems involved in value representation (prefrontal cortex, PFC) and reinforcement learning (basal ganglia, BG) as well as to the compromised connectivity of these regions. In this study, we employed learning tasks hypothesized to probe the function and interaction of the PFC and BG in patients with SZ-spectrum disorders in comparison to healthy control (HC) subjects. In the Instructed Probabilistic Selection task (IPST), participants received false instruction about one of the stimuli used in the course of probabilistic learning which creates confirmation bias, whereby the instructed stimulus is overvalued in comparison to its real experienced value. The IPST was administered to 102 patients with SZ and 120 HC subjects. We have shown that SZ patients and HC subjects were equally influenced by false instruction in reinforcement learning (RL) probabilistic task (IPST) (p-value = 0.441); however, HC subjects had significantly higher learning rates associated with the process of overcoming cognitive bias in comparison to SZ patients (p-value = 0.018). The behavioral results of our study could be hypothesized to provide further evidence for impairments in the SZ-BG circuitry; however, this should be verified by neurofunctional imaging studies.

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“…The DMN, which is comprised of the medial prefrontal cortex (MPFC), medial temporal lobes (MTLs), and posterior cingulate cortex (PCC)/retropslenial cortex (RSC) (145,152), is preferentially activated when an individual is not engaged with the external world (153); it is what the brain does when it is not specifically focused on doing anything else but is considering oneself [ex., remembering, considering hypothetical social interactions, thinking of one's own future (153)]. Perturbations in these networks, including the DMN, are often associated with disorders of thought and consciousness (133,(153)(154)(155)(156)(157)(158). What, then, does ketamine do to the DMN?…”

Section: What Does Functional Imaging Tell Us About Ketamine Depressi...mentioning

confidence: 99%

Targeting Affective Mood Disorders With Ketamine to Prevent Chronic Postsurgical Pain

Willis

Goldstein

2022

Front. Pain Res.

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The phencyclidine-derivative ketamine [2-(2-chlorophenyl)-2-(methylamino)cyclohexan-1-one] was added to the World Health Organization's Model List of Essential Medicines in 1985 and is also on the Model List of Essential Medicines for Children due to its efficacy and safety as an intravenous anesthetic. In sub-anesthetic doses, ketamine is an effective analgesic for the treatment of acute pain (such as may occur in the perioperative setting). Additionally, ketamine may have efficacy in relieving some forms of chronic pain. In 2019, Janssen Pharmaceuticals received regulatory-approval in both the United States and Europe for use of the S-enantiomer of ketamine in adults living with treatment-resistant major depressive disorder. Pre-existing anxiety/depression and the severity of postoperative pain are risk factors for development of chronic postsurgical pain. An important question is whether short-term administration of ketamine can prevent the conversion of acute postsurgical pain to chronic postsurgical pain. Here, we have reviewed ketamine's effects on the biopsychological processes underlying pain perception and affective mood disorders, focusing on non-NMDA receptor-mediated effects, with an emphasis on results from human trials where available.

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Functional brain network dysfunctions in subjects at high-risk for psychosis: A meta-analysis of resting-state functional connectivity

Cited by 39 publications

References 84 publications

Overlap across psychotic disorders: A functional network connectivity analysis

Overlap across psychotic disorders: A functional network connectivity analysis

Confirmation Bias in the Course of Instructed Reinforcement Learning in Schizophrenia-Spectrum Disorders

Targeting Affective Mood Disorders With Ketamine to Prevent Chronic Postsurgical Pain

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