Altered auditory processing and effective connectivity in 22q11.2 deletion syndrome
Introduction
The 22q11.2 deletion is one of the most common copy number variants (CNV) with a prevalence of 1:2000 to 1:4000 (Goodship et al., 1998; Oskarsdóttir et al., 2004; Shprintzen, 2005). The 22q11.2 deletion syndrome (22q11.2DS) is characterized by multiple somatic disorders, cognitive deficits and learning disabilities (Karayiorgou et al., 2010; Robin and Shprintzen, 2005). Further, the syndrome is associated with hearing loss (Jiramongkolchai et al., 2016). Recent studies have shown that people carrying the deletion are at higher risk for several neurodevelopmental disorders including autism, ADHD, and schizophrenia, (Bassett et al., 2008; Karayiorgou et al., 2010; Purcell et al., 2009; Schneider et al., 2014; Stefansson et al., 2008). Clinical observation studies have shown that approximately 25% of the carriers meet diagnostic criteria for schizophrenia by adulthood (Schneider et al., 2014) and with odds ratios above 16, the deletion is one of the largest known risk factors for schizophrenia (Marshall et al., 2016; Szatkiewicz et al., 2014). In addition, results from a new nationwide Danish study showed that people diagnosed with 22q11.2DS had six to eight times higher risk of developing schizophrenia spectrum disorders as compared to the general population (Hoeffding et al., 2017; Vangkilde et al., 2016b). For this reason, investigating the neurobiology of 22q11.2 deletion carriers can provide important insights into the pathogenesis of schizophrenia and potential disease risk markers.
It is well established that people with schizophrenia show a reduced mismatch negativity (MMN) at fronto-central electrodes over the scalp when assessed with electroencephalography (EEG) (Catts et al., 1995; Michie, 2001; Näätänen and Kähkönen, 2009; Umbricht and Krljesb, 2005). MMN is evoked in oddball paradigms, whereby standard stimuli form a rule that is occasionally violated by oddball events. Defined as the negative deflection in the event-related potential peaking around 100–250 ms after the change onset, the MMN emerges when subtracting the response to a standard tone from the response to a deviant tone (Näätänen, 1995; Näätänen et al., 2007). MMN is not only reduced in chronic schizophrenia but also in first episode psychosis, (Atkinson et al., 2012; Hsieh et al., 2012), first degree relatives (Jessen et al., 2001; Michie et al., 2002) and further shown to be a promising biomarker for psychosis prediction (Bodatsch et al., 2015), see also (Randeniya et al., 2017) for a review on MMN in the continuum of psychosis. Only a limited number of studies have investigated MMN in 22q11.2 deletion carriers (Baker et al., 2005; Zarchi et al., 2013). Baker and colleagues (Baker et al., 2005) found reduced duration MMN consistent with findings in the schizophrenia literature (Baldeweg et al., 2002). In contrast, Zarchi et al. (2013) failed to replicate this finding but found that Gap-MMN amplitudes in the 22q11.2DS group predicted the negative symptoms scores (from the Positive and Negative Syndrome Scale, PANSS) where smaller MMN amplitudes were associated with higher scores of the PANSS. Notably, the disease states of the 22q11.2DS groups in the two mentioned studies deviate from each other. In (Baker et al., 2005) no participants met criteria for a diagnosis of psychotic disorder, whereas in (Zarchi et al., 2013) a proportion of the participants (14.63%) were diagnosed with psychotic disorders and three of these met the DSM-IV-TR for schizophrenia.
Approaches to modelling MMN using Dynamic Causal Modelling (DCM) have viewed the underlying mechanism of MMN in terms of the predictive coding hypothesis (Garrido et al., 2008; Rao and Ballard, 1999). In this way, MMN is caused by an interplay between current inputs and predictions based on a learnt regularity (Garrido et al., 2009a). The network implementation of these processes involve bottom-up and top-down connections that link lower- with higher-level sensory areas (Friston, 2003). This interplay appears to be disrupted in schizophrenia (Adams et al., 2013; Dima et al., 2012, Dima et al., 2010; Fogelson et al., 2014) as well as in unaffected relatives (Ranlund et al., 2016) especially in top-down processing, i.e., connections from higher to lower order areas. Since functional disintegration among brain regions phrased as “The disconnection hypothesis” is believed to be one of the core pathologies of psychosis (Friston, 1998), we use DCM in addition to conventional MMN analysis in sensor space, to test this notion of disconnectivity in the 22q11DS population.
In this study, the neuronal connectivity underlying change detection was assessed in a group of young non-psychotic 22q11.2 deletion carriers as well as in a healthy age- and sex-comparable control group using DCM. Given that schizophrenia patients show reduced MMN responses, and that 22q11.2DS are a schizophrenia high-risk group, we hypothesized that the 22q11.2 deletion carriers would also express a reduction in MMN responses. Based on previous identified neural generators of MMN, we formulated families of DCMs according to their type of connections, to test the hypothesis that 22q11.2 deletion carriers would afford reduced top-down connectivity within the network accounting for MMN, compared to healthy non-carriers. Finally, we explored whether effective connectivity in 22q11.2 deletion carriers as well as MMN amplitudes were associated with the individual symptoms score in the 22q11.2 deletion carriers.
Section snippets
Participants
We included 19 22q11.2 deletion carriers without a current or previous history of schizophrenia. All carriers had a verified deletion within the 3 Mb region at chromosome 22q11.2. Our control group included 27 healthy individuals without the 22q11.2 deletion. Groups were comparable with respect to sex ratio (male/female controls: 18/9, carriers: 13/6, χ2 = 0.02, p = 0.90) and age distribution (controls age range: 12–25 years; mean age: 15.96, standard deviation (SD) = 2.71 years; 22q11.2 age
Mismatch negativity responses
Fig. 3 shows the grand average data for the conventional MMN analysis for a selected channel (Fz, Fig. 3 A–E) and the whole scalp (Fig. 3F). The third standard tone, S3, yields the largest MMN in the pooled sample (Fig. 3A), hence we used it as the standard for all subsequent analysis. The mean MMN amplitude as a function of tone repetition followed the shape of a parabola, indicating that surprise builds up until S3, after which it decreases, possibly because a change starts to be expected.
Discussion
In this study, we investigated the responses elicited by a roving auditory MMN paradigm in a group of young 22q11.2 deletion carriers. While we found no indication of group differences between the MMN responses per se, the spatiotemporal analysis of responses to tones (standards and deviants) revealed a main effect of group in the fronto-central areas peaking at 90 ms. This group difference was due to the 22q11.2 deletion carriers exhibiting larger negative responses in the N1 component, which
Contribution
All authors contributed in the design of the study. KML and MRB collected the data. KML analysed the data and wrote the first draft of the manuscript. AV, HS and LO recruited the subjects and administered clinical assessment. All authors contributed to the interpretation of the data, revised the manuscript and agreed with the final content of the manuscript.
Funding
This study was funded by the Lundbeck Foundation, Denmark (R155-2014-1724); Lundbeck Foundation [Grant of Excellence “ContAct” R59 A5399]; Lundbeck Foundation fellowship (R105–9813); The Capital Region's Research Foundation for Mental Health Research.
Conflict of interest
H.R.S. received honoraria as speaker from Lundbeck A/S, Valby, Denmark, Biogen Idec, Denmark A/S, Genzyme, Denmark and MerckSerono, Denmark, honoraria as editor from Elsevier Publishers, Amsterdam, The Netherlands and Springer Publishing, Stuttgart, Germany, travel support from MagVenture, Denmark, and grant support from Biogen Idec, Denmark A/S. M.R.B is a prior employee at H. Lundbeck A/S, Denmark and received financial support for her PhD from the Innovation Fund Denmark. M.D. is employed
Acknowledgements
We would like to thank all the participants and their families for taking the time to participate in this study and the Danish National 22q11DS Association for their strong support of our work. We would also like to express our gratitude to the staff involved in the Danish Blood Donor Study, Capital Region Blood bank, Glostrup. Further, we would like to thank http://www.forsogsperson.dk from which some of our control participants were recruited from. M.I.G acknowledges support from any
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