Elevated P3b latency variability in carriers of ZNF804A risk allele for psychosis

Saville, Christopher W. N.; Lancaster, Thomas; Davies, Timothy; Toumaian, Maida; Pappa, Eleni; Fish, Simon; Feige, Bernd; Bender, Stephan; Mantripragada, Kiran Kumar; Linden, David Edmund Johannes; Klein, Christoph

doi:10.1016/j.neuroimage.2015.04.024

Cited by 13 publications

(9 citation statements)

References 49 publications

(70 reference statements)

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“…Each participant was assigned to two groups: a diagnosis group (SZ, BD or control) and, after genotyping (see below), a genotype group (ZNF+ which included risk allele (A) homozygotes, or ZNF− which included heterozygotes and non-risk allele (C) homozygotes). Again, the merge within ZNF− had the purpose of maximizing counterbalance for this SNP (as is commonly practiced in the literature e.g., Kuswanto et al, 2012b ; Schultz et al, 2014 ; Donohoe et al, 2011 ; Saville et al, 2015 ), given the very low frequency of allele C in the Caucasian population.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide discovered psychosis-risk gene ZNF804A impacts on white matter microstructure in health, schizophrenia and bipolar disorder

Mallas

Carletti

Chaddock

et al. 2016

PeerJ

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Background. Schizophrenia (SZ) and bipolar disorder (BD) have both been associated with reduced microstructural white matter integrity using, as a proxy, fractional anisotropy (FA) detected using diffusion tensor imaging (DTI). Genetic susceptibility for both illnesses has also been positively correlated in recent genome-wide association studies with allele A (adenine) of single nucleotide polymorphism (SNP) rs1344706 of the ZNF804A gene. However, little is known about how the genomic linkage disequilibrium region tagged by this SNP impacts on the brain to increase risk for psychosis. This study aimed to assess the impact of this risk variant on FA in patients with SZ, in those with BD and in healthy controls.Methods. 230 individuals were genotyped for the rs1344706 SNP and underwent DTI. We used tract-based spatial statistics (TBSS) followed by an analysis of variance, with threshold-free cluster enhancement (TFCE), to assess underlying effects of genotype, diagnosis and their interaction, on FA.Results. As predicted, statistically significant reductions in FA across a widely distributed brain network (p < 0.05, TFCE-corrected) were positively associated both with a diagnosis of SZ or BD and with the double (homozygous) presence of the ZNF804A rs1344706 risk variant (A). The main effect of genotype was medium (d = 0.48 in a 44,054-voxel cluster) and the effect in the SZ group alone was large (d = 1.01 in a 51,260-voxel cluster), with no significant effects in BD or controls, in isolation. No areas under a significant diagnosis by genotype interaction were found.Discussion. We provide the first evidence in a predominantly Caucasian clinical sample, of an association between ZNF804A rs1344706 A-homozygosity and reduced FA, both irrespective of diagnosis and particularly in SZ (in overlapping brain areas). This suggests that the previously observed involvement of this genomic region in psychosis susceptibility, and in impaired functional connectivity, may be conferred through it inducing abnormalities in white matter microstructure.

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Section: Methodsmentioning

confidence: 99%

Genome-wide discovered psychosis-risk gene ZNF804A impacts on white matter microstructure in health, schizophrenia and bipolar disorder

Mallas

Carletti

Chaddock

et al. 2016

PeerJ

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“…Amplitude and latency were measured at that time point. Trials with measured peak latency at the beginning or the end of the peak search window were excluded from analysis as these were not likely to be associated with a local minimum, as has been done before in single trial analyses (Saville et al, 2015b ). Besides measuring the N1 amplitude in relation to the pre-stimulus baseline, we also measured N1 peak-to-peak amplitude in relation to the maximum positive amplitude value between 50 ms after stimulus onset and the N1 latency (P1).…”

Section: Methodsmentioning

confidence: 99%

Spontaneous Fluctuations in Sensory Processing Predict Within-Subject Reaction Time Variability

Ribeiro

Paiva²

2016

Front. Hum. Neurosci.

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When engaged in a repetitive task our performance fluctuates from trial-to-trial. In particular, inter-trial reaction time variability has been the subject of considerable research. It has been claimed to be a strong biomarker of attention deficits, increases with frontal dysfunction, and predicts age-related cognitive decline. Thus, rather than being just a consequence of noise in the system, it appears to be under the control of a mechanism that breaks down under certain pathological conditions. Although the underlying mechanism is still an open question, consensual hypotheses are emerging regarding the neural correlates of reaction time inter-trial intra-individual variability. Sensory processing, in particular, has been shown to covary with reaction time, yet the spatio-temporal profile of the moment-to-moment variability in sensory processing is still poorly characterized. The goal of this study was to characterize the intra-individual variability in the time course of single-trial visual evoked potentials and its relationship with inter-trial reaction time variability. For this, we chose to take advantage of the high temporal resolution of the electroencephalogram (EEG) acquired while participants were engaged in a 2-choice reaction time task. We studied the link between single trial event-related potentials (ERPs) and reaction time using two different analyses: (1) time point by time point correlation analyses thereby identifying time windows of interest; and (2) correlation analyses between single trial measures of peak latency and amplitude and reaction time. To improve extraction of single trial ERP measures related with activation of the visual cortex, we used an independent component analysis (ICA) procedure. Our ERP analysis revealed a relationship between the N1 visual evoked potential and reaction time. The earliest time point presenting a significant correlation of its respective amplitude with reaction time occurred 175 ms after stimulus onset, just after the onset of the N1 peak. Interestingly, single trial N1 latency correlated significantly with reaction time, while N1 amplitude did not. In conclusion, our findings suggest that inter-trial variability in the timing of extrastriate visual processing contributes to reaction time variability.

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“…Single-trial event-related potentials (Saville et al, 2011a, Saville et al, 20142015b) offer an opportunity to investigate this. By identifying neural markers of covert processing steps in each trial we can estimate the latency distributions of sub-processes that underlie an RT to identify whether each subcomponent displays a worst performance rule pattern.…”

Section: Introductionmentioning

confidence: 99%

A neural analogue of the worst performance rule: Insights from single-trial event-related potentials

et al. 2016

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The worst performance rule is the tendency for participants' slowest reaction times to correlate more with psychometric intelligence than their faster reaction times. Reaction times, however, are influenced by the duration of multiple perceptual, attentional, and motor sub-processes, and it is unclear whether the same pattern exists in these sub-processes as well. We used single-trial event-related potentials to identify whether a worst performance rule pattern could be found in stimulus and response-locked P3b latency distributions and scores on a test of non-verbal psychometric intelligence. Fifty participants carried out a set of working memory oddball tasks, while electroencephalographic data were collected, and the British Version of the Intelligence Structure Test, in a separate session. Single-trial P3b latencies were identified in stimulus and response-locked data and a novel quantile bootstrapping method was used to identify which quantiles of the P3b latency distributions correlated most with test scores. In stimulus-locked data, correlations between quantile mean and test scores became more negative with increasing quantile, showing clear evidence of a worst performance rule pattern. In response-locked data, low scorers showed more extreme latencies in both tails of the distribution. However we did not observe a worst performance rule in behavioural data. These data suggest that psychometric intelligence is also associated with response-related processes, which may also contribute to the association between psychometric intelligence and reaction time variability.

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Elevated P3b latency variability in carriers of ZNF804A risk allele for psychosis

Cited by 13 publications

References 49 publications

Genome-wide discovered psychosis-risk gene ZNF804A impacts on white matter microstructure in health, schizophrenia and bipolar disorder

Genome-wide discovered psychosis-risk gene ZNF804A impacts on white matter microstructure in health, schizophrenia and bipolar disorder

Spontaneous Fluctuations in Sensory Processing Predict Within-Subject Reaction Time Variability

A neural analogue of the worst performance rule: Insights from single-trial event-related potentials

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