Abnormally increased and incoherent resting-state activity is shared between patients with schizophrenia and their unaffected siblings
Introduction
Psychotic symptoms, including false attribution of perceptual experience to external sources (hallucinations), grossly distorted thinking (delusions), and disorganized speech and behavior, are recognized as a defining feature of schizophrenia (DSM-5) (Association, 2013). It has been widely accepted that schizophrenic psychosis is closely related to disrupted self-generated mental activity at rest (Siegal and Varley, 2002). For instance, paranoid ideas regarding conspiracy may reflect an exaggerated sense of self-relevance. Thus, mental activity occurring during rest, in the absence of cognitive tasks, has been thought to be relevant to the clinical phenomenology of schizophrenia (Malaspina et al., 2004).
In the past, positron emission tomography (PET) and electroencephalography (EEG) have been the most commonly used techniques to investigate the spontaneous neuronal activity (Logothetis et al., 2001) at rest in schizophrenia. These studies have consistently demonstrated metabolic over-activity (Ebmeier et al., 1993, Kaplan et al., 1993, Parellada et al., 1994) and increased resting-state electric activity (Boutros et al., 2008, Knott et al., 2001, Lee et al., 2006) in schizophrenia. Importantly, this resting hyperactivity is associated with psychotic symptoms (Lee et al., 2006) such as reality distortion (Kaplan et al., 1993, Liddle et al., 1992). Recently, the amplitude of low-frequency fluctuations (ALFF) in the blood oxygen level-dependent (BOLD) signal measured by the fMRI has emerged as a consistent marker of spontaneous neuronal activity (Biswal et al., 1995, Logothetis et al., 2001, Raichle, 2006, Yu-Feng et al., 2007). Robust evidence has indicated the involvement of ALFF in various cognitive functions, including reading skills (Xu et al., 2015), conceptual processing (Wei et al., 2012), working memory (Zou et al., 2012), cognitive control and response inhibition (Mennes et al., 2010) and personality traits (Wei et al., 2012). Consistent with the findings using PET and EEG, resting-state fMRI studies applying ALFF have documented abnormally increased activity in various brain regions in schizophrenia (Hoptman et al., 2010, Huang et al., 2010, Yu et al., 2014). The combined evidence from PET, EEG and fMRI studies suggests that exaggerated brain activity at rest may be a potential neural substrate for schizophrenia.
Furthermore, numerous resting-state fMRI (Lawrie et al., 2002, Liu et al., 2006, Pettersson-Yeo et al., 2011, Yu et al., 2013) and EEG (Ford et al., 2002, Ford et al., 2008, Gross et al., 2007) studies have revealed schizophrenia as a functional “disconnectivity” disorder, concurring with Blueler's hypothesis of this disorder as mentally “splitting” (Bleuler, 1950). These abnormal functional connections have been documented to be associated with symptoms including hallucinations, delusions, distorted thinking and speech (Lawrie et al., 2002, MacDonald et al., 2005, Yoon et al., 2008). However, most previous studies have examined the synchrony of low-frequency fluctuations (LFFs) between remote brain regions (i.e., functional connectivity, FC). From this approach, no conclusion can be drawn on which region has the primary dysfunction. Recently, regional homogeneity (Reho) reflecting synchrony of LFFs (Zang et al., 2004) within circumscribed brain regions has been applied to evaluate regional brain function. Evidence of reduced Reho in recent resting-state fMRI studies (Liu et al., 2006, Yu et al., 2013) has indicated that the local synchrony of spontaneous neuronal activity is a key neuropathological feature of schizophrenia. Additionally, abnormal Reho has been found to be associated with impaired ability of specific information processing and integration in schizophrenia (Yu et al., 2013).
Collectively, the evidence on intensity and synchrony of spontaneous neuronal activity suggests that schizophrenia may be characterized by increased (Boutros et al., 2008, Yu et al., 2014) but incoherent neuronal activity (Liu et al., 2006, Yu et al., 2013) at rest. However, previous studies investigated regional brain function in terms of intensity and synchrony of the LFFs separately; thus, no evidence on both increased amplitude and reduced synchrony of spontaneous neuronal activity in the same dataset of schizophrenia patients has been documented. In this study, we combined the ALFF and Reho on resting-state fMRI data to investigate the intensity and synchrony of spontaneous neuronal activity and expected to observe excessive and incoherent neuronal activity at rest in schizophrenia.
Furthermore, a growing body of evidence indicates that schizophrenia is a neurodevelopmental disorder with high heritability (Keshavan et al., 2005, Raedler et al., 1998), suggesting that neural deficits related to psychosis may be present prior to the manifestation of behavioral/clinical symptoms in the unaffected siblings of patients (Keshavan et al., 2005, Raedler et al., 1998, Weinberger, 1987). Unaffected siblings of schizophrenia share half of their genotype with their ill relatives and are at an 8-fold higher risk for developing schizophrenia than the general population (Sadock and Sadock, 2011). Clinical evidence has consistently demonstrated that unaffected siblings of patients display mild psychotic and cognitive symptoms (Bediou et al., 2007, Chen et al., 2009, Delawalla et al., 2006). Genetic predisposition for schizophrenia may also affect normal brain functions in individuals at high-risk for schizophrenia (Whitfield-Gabrieli et al., 2009b). For example, elevated brain activity and reduced resting-state FC have been found in individuals at risk for schizophrenia (Guo et al., 2014, Howes et al., 2006) and individuals with prodromal symptoms of schizophrenia (Dandash et al., 2013, Howes et al., 2009). Notably, recent studies have showed increased intensity (Tang et al., 2015) and reduced synchrony (Liao et al., 2012) of spontaneous neuronal activity using ALFF and Reho, respectively, in unaffected families of schizophrenia patients. Based on these findings, we hypothesized that excessive and incoherent spontaneous neuronal activity seen in patients would be shared by their unaffected siblings.
Section snippets
Participants
The sample in this study includes 27 patients, 27 siblings and 27 healthy participants, with most of the participants coming from the dataset in our prior work (Liu et al., 2010). All participants were right-handed (Annett, 1970). 27 patients, independently diagnosed with schizophrenia (SCZ) based on the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) Axis I Disorders, Patient Edition (SCID-I/P) (First et al., 1998), were recruited through the Department of
Demographic and clinical characteristics
The characteristics of SCZ, SIB and HCs groups are summarized in Table 1. The three groups had no significant differences in age (F(2, 79) = 0.793, p = 0.696), years of education (F(2, 79) = 0.365, p = 0.456) or gender (χ2 = 0.713, p = 0.700).
Aberrant Reho in SCZ and SIB compared to HCs
For the Reho, one-way ANOVA (puncorrected < 0.001) revealed significant differences in the left middle temporal gyrus (MTG), orbito-frontal gyrus (OFG), inferior occipital gyrus (IOG), calcarine and middle occipital gyrus (MOG), and the right pre-central gyrus (PrCG)
Discussion
In the present study, we observed that excessive and incoherent regional spontaneous neuronal activity in the left MTG and OFG, along with over-activity in the left FIG and incoherent neuronal activity in the left IOG, were shared between schizophrenia patients and their unaffected siblings. These findings indicate that the excessive and incoherent regional spontaneous neuronal activity at rest may be a potential neurophysiological endophenotype for schizophrenia. Meanwhile, schizophrenia
Conclusion
In summary, the present study suggests that schizophrenia and their unaffected siblings share increased and incoherent neuronal activity in local brain regions related to language processing, social cognition and emotion-related functioning and visual processing. Our findings regarding these common brain abnormalities observed in schizophrenia and their siblings may offer a framework for examining effects of genetic diathesis on the development of schizophrenia.
Conflict of interest
The authors declare no conflict of interest.
Contributions
Dr Pu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Weidan Pu designed the study.
Chang Liu and Weidan Pu analyzed, interpreted the data and wrote the first draft of the manuscript.
Zhimin Xue and Zhening Liu provided fMRI technical support and revised it critically for important intellectual content.
Lena Palaniyappan revised it critically for important intellectual content.
Zhimin Xue, Li Zhou, Haihong
Role of funding source
This work was supported by the National Natural Science Foundation of China (81401125 to W.P., 81271485, 81471362 and 81561168021 to Z.N. Liu, 81171287 to Z.X.) and Hunan Provincial Innovation Foundation for Postgraduate (CX2014B106).
Acknowledgment
We thank Zhong He, the Second Xiangya hospital, Central South University, for his assistance in fMRI data acquisition.
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2021, Journal of Psychiatric ResearchCitation Excerpt :Furthermore, higher levels of the oxidative stress marker protein carbonyl content was observed in SB compared to HC (Massuda et al., 2013b), implying that oxidative stress unbalance might also be present in SB. Additionally, neuroimaging and neuropsychological studies also display some abnormalities in siblings compared to healthy controls and individuals with schizophrenia, especially in brain connectivity and resting-state modes (Chang et al., 2014; Collin et al., 2014; Liu et al., 2016; Zalesky et al., 2015), and in cognitive functioning (Islam et al., 2018; Massuda et al., 2013a). Interestingly, most studies assessing unaffected siblings of subjects with schizophrenia using ML algorithms utilize neuroimaging techniques (Antonucci et al., 2020; Guo, S. et al., 2014; Guo et al., 2017; Guo, W. et al., 2014; Pergola et al., 2017).
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