Abnormalities of regional homogeneity and its correlation with clinical symptoms in Naïve patients with first-episode schizophrenia

Zhao, Xiaojie; Yao, Jingjing; Lv, Yiding; Zhang, Xinyue; Han, Chongyang; Chen, Lijun; Ren, Fangfang; Zhou, Jin; Li, Yuan; Sui, Yuxiu

doi:10.1007/s11682-018-9882-4

Cited by 42 publications

(32 citation statements)

References 73 publications

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“…Moreover, we observed a decreased dALFF variability of the right post-central gyrus, which is consistent with previous fMRI studies showing disrupted local functional integration patterns of the post-central gyrus in schizophrenia ( Jiang et al, 2015 ; Zhao et al, 2018 ). Previous studies also identified concurrent changes in structural and dynamic functional integration in the supramarginal gyrus, which were both effected by illness and age in EOS patients ( Thormodsen et al, 2013 ; Wang et al, 2019c ).…”

Section: Discussionsupporting

confidence: 92%

Dynamic Alterations of Amplitude of Low-Frequency Fluctuations in Patients With Drug-Naïve First-Episode Early Onset Schizophrenia

Cao

et al. 2020

Front. Neurosci.

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Abnormalities in static neural activity have been widely reported in early onset schizophrenia (EOS). However, dynamic brain activity alterations over time in EOS are unclear. Here, we investigated whether temporal dynamic changes in spontaneous neural activity are influenced by EOS. A total of 78 drug-naïve first-episode patients with EOS and 90 healthy controls (HCs) were enrolled in this study. Dynamic amplitude of low-frequency fluctuations (dALFF) was performed to examine the abnormal time-varying local neural activity in EOS. Furthermore, we investigated the relationships between abnormalities in dALFF variability and clinical characteristics in EOS patients. Compared to HCs, EOS patients showed significantly decreased dALFF variability in the bilateral precuneus, right superior marginal gyrus, right post-central gyrus and increased dALFF in the right middle temporal gyrus (MTG). Moreover, increased dALFF variability in MTG was negatively associated with negative symptoms in EOS. Our findings reveal increased dynamic local neural activity in higher order networks of the cortex, suggesting that enhanced spontaneous brain activity may be a predominant neural marker for brain maturation. In addition, decreased dALFF variability in the default mode network (DMN) and limbic system may reflect unusually dynamic neural activity. This dysfunctional brain activity could distinguish between patients and HCs and deepen our understanding of the pathophysiological mechanisms of EOS.

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Section: Discussionsupporting

confidence: 92%

Dynamic Alterations of Amplitude of Low-Frequency Fluctuations in Patients With Drug-Naïve First-Episode Early Onset Schizophrenia

Cao

et al. 2020

Front. Neurosci.

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“…Increased and decreased ReHo values represent abnormal neural activities (11). According to previous studies on subjects with a high risk of schizophrenia (12,13), first-episode adolescentonset drug-naïve schizophrenia (14,15), first-episode drug-naïve schizophrenia (16), chronic schizophrenia (17), and treatmentresistant schizophrenia (18), abnormal ReHo may be a good biomarker to distinguish patients with schizophrenia from healthy controls with increased or decreased ReHo values in different regions. However, previous results were inconsistent.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, abnormal neural activities are associated with clinical symptoms and cognitive dysfunction, and ReHo may be used to evaluate the severity of clinical symptoms and cognitive dysfunction. With regard to clinical symptoms, abnormal ReHo values in several brain regions of patients with schizophrenia are positively/negatively/not associated with the Positive and Negative Syndrome Scale (PANSS) total score, positive factor, disorganized/concrete factor, excited factor and depressed factor (14)(15)(16)19). In term of cognitive dysfunction, a study on subjects with a genetically high risk of schizophrenia has demonstrated that delayed recall is negatively associated with decreased ReHo values in the right superior frontal gyrus.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Prefrontal Regional Homogeneity and Its Correlations With Cognitive Dysfunction/Psychopathology in Patients With First-Diagnosed and Drug-Naive Schizophrenia

et al. 2020

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Background: Schizophrenia, regarded as a neurodevelopmental disorder, is characterized by positive symptoms, negative symptoms, and cognitive dysfunction. Investigating the spontaneous brain activity in patients with schizophrenia can help us understand the underlying pathophysiologic mechanism of schizophrenia. However, results concerning abnormal neural activities and their correlations with cognitive dysfunction/psychopathology of patients with schizophrenia were inconsistent. Methods: We recruited 57 first-diagnosed and drug-naive patients with schizophrenia and 50 matched healthy controls underwent magnetic resonance imaging. The Positive and Negative Syndrome Scale (PANSS) and the MATRICS Consensus Cognitive Battery were used to assess the psychopathology/cognitive dysfunction. Regional homogeneity (ReHo) was used to explore neural activities. Correlation analyses were calculated between abnormal ReHo values and PANSS scores/standardized cognitive scores. Lastly, support vector machine analyses were conducted to evaluate the accuracy of abnormal ReHo values in distinguishing patients with schizophrenia from healthy controls. Results: Patients with schizophrenia showed cognitive dysfunction, and increased ReHo values in the right gyrus rectus, right inferior frontal gyrus/insula and left inferior frontal gyrus/insula compared with those of healthy controls. The ReHo values in the right inferior frontal gyrus/insula were positively correlated with negative symptom scores and negatively correlated with Hopkins verbal learning test-revised/verbal learning. Our results showed that the combination of increased ReHo values in the left inferior frontal gyrus/insula and right gyrus rectus had 78.5% (84/107) accuracy, 85.96% (49/57) sensitivity, and 70.00% specificity, which were higher than other combinations. Gao et al. ReHo and Cognition/Psychopathology in Schizophrenia Conclusions: Hyperactivities were primarily located in the prefrontal regions, and increased ReHo values in the right inferior frontal gyrus/insula might reflect the severity of negative symptoms and verbal learning abilities. The combined increases of ReHo values in these regions might be an underlying biomarker in differentiating patients with schizophrenia from healthy controls.

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“…The limited prior work on predicting schizophrenia symptoms via machine learning has thus far only been performed on the basis of composite symptoms (10,11), general functioning (12,13) and polygenic risk scores for schizophrenia (14). Other neuroimaging studies have also reported univariate correlates (15,16), or lack thereof (17,18), with symptom severity on the basis of composite summary scores rather than those of the underlying symptoms, an approach which significantly comprises aetiological specificity (19). For example, if we were to compare two patients, one with disorganised thought processes which render them unable to bathe themselves to another with severe alogia who is unable to communicate, these are vastly different symptoms which in turn are likely to be caused by different sources of dysfunction in different neural networks.…”

Section: Introductionmentioning

confidence: 99%

Multi-dimensional predictions of psychotic symptoms via machine learning

Taylor

Larsen

Garrido

2020

Preprint

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BackgroundThe diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. MethodsAlthough machine learning techniques are considered a potential gateway to precision psychiatry, prior work has primarily focused on dichotomous patient-control classification.Instead, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. ResultsBrain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Each of these symptoms was associated with alterations in specific functional networks encompassing the ventral and dorsal attention networks, the auditory network, amongst other cortical and subcortical regions. We found that an ensemble of subscale models yielded a two-fold increase in accuracy over single models which predict positive and negative compound scores directly. ConclusionsOur results suggest that modelling symptoms as an ensemble of subscales is more accurate, specific, and informative than the compound-based approach. We provide functional brain maps of model contributions identifying the networks of regions which pertain to each individual symptom. This approach is transferrable to any other psychiatric condition and may also contribute to the development of precision psychiatry.

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Abnormalities of regional homogeneity and its correlation with clinical symptoms in Naïve patients with first-episode schizophrenia

Cited by 42 publications

References 73 publications

Dynamic Alterations of Amplitude of Low-Frequency Fluctuations in Patients With Drug-Naïve First-Episode Early Onset Schizophrenia

Dynamic Alterations of Amplitude of Low-Frequency Fluctuations in Patients With Drug-Naïve First-Episode Early Onset Schizophrenia

Enhanced Prefrontal Regional Homogeneity and Its Correlations With Cognitive Dysfunction/Psychopathology in Patients With First-Diagnosed and Drug-Naive Schizophrenia

Multi-dimensional predictions of psychotic symptoms via machine learning

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