Elizabeth B. Liddle

Neural Primacy of the Salience Processing System in Schizophrenia

Summary For effective information processing, two large-scale distributed neural networks appear to be critical: a multimodal executive system anchored on the dorsolateral prefrontal cortex (DLPFC) and a salience system anchored on the anterior insula. Aberrant interaction among distributed networks is a feature of psychiatric disorders such as schizophrenia. We used whole-brain Granger causal modeling using resting fMRI and observed a significant failure of both the feedforward and reciprocal influence between the insula and the DLPFC in schizophrenia. Further, a significant failure of directed influence from bilateral visual cortices to the insula was also seen in patients. These findings provide compelling evidence for a breakdown of the salience-execution loop in the clinical expression of psychosis. In addition, this offers a parsimonious explanation for the often-observed “frontal inefficiency,” the failure to recruit prefrontal system when salient or novel information becomes available in patients with schizophrenia.

Task-Related Default Mode Network Modulation and Inhibitory Control in ADHD: Effects of Motivation and Methylphenidate.

BACKGROUND Deficits characteristic of attention deficit/hyperactivity disorder (ADHD), including poor attention and inhibitory control, are at least partially alleviated by factors that increase engagement of attention, suggesting a hypodopaminergic reward deficit. Lapses of attention are associated with attenuated deactivation of the default mode network (DMN), a distributed brain system normally deactivated during tasks requiring attention to the external world. Task-related DMN deactivation has been shown to be attenuated in ADHD relative to controls. We hypothesised that motivational incentives to balance speed against restraint would increase task engagement during an inhibitory control task, enhancing DMN deactivation in ADHD. We also hypothesised that methylphenidate, an indirect dopamine agonist, would tend to normalise abnormal patterns of DMN deactivation. METHOD We obtained functional magnetic resonance images from 18 methylphenidate-responsive children with ADHD (DSM-IV combined subtype) and 18 pairwise-matched typically developing children aged 9-15 years while they performed a paced Go/No-go task. We manipulated motivational incentive to balance response speed against inhibitory control, and tested children with ADHD both on and off methylphenidate. RESULTS When children with ADHD were off-methylphenidate and task incentive was low, event-related DMN deactivation was significantly attenuated compared to controls, but the two groups did not differ under high motivational incentives. The modulation of DMN deactivation by incentive in the children with ADHD, off-methylphenidate, was statistically significant, and significantly greater than in typically developing children. When children with ADHD were on-methylphenidate, motivational modulation of event-related DMN deactivation was abolished, and no attenuation relative to their typically developing peers was apparent in either motivational condition. CONCLUSIONS During an inhibitory control task, children with ADHD exhibit a raised motivational threshold at which task-relevant stimuli become sufficiently salient to deactivate the DMN. Treatment with methylphenidate normalises this threshold, rendering their pattern of task-related DMN deactivation indistinguishable from that of typically developing children.

Effects of Motivation and Medication on Electrophysiological Markers of Response Inhibition in Children with Attention-Deficit/Hyperactivity Disorder

Background Theories of attention-deficit/hyperactivity disorder (ADHD) posit either executive deficits and/or alterations in motivational style and reward processing as core to the disorder. Effects of motivational incentives on electrophysiological correlates of inhibitory control and relationships between motivation and stimulant medication have not been explicitly tested. Methods Children (9–15 years) with combined-type ADHD (n = 28) and matched typically developing children (CTRL) (n = 28) performed a go/no-go task. Electroencephalogram data were recorded. Amplitude of two event-related potentials, the N2 and P3 (markers of response conflict and attention), were measured. The ADHD children were all stimulant responders tested on and off their usual dose of methylphenidate; CTRLs were never medicated. All children performed the task under three motivational conditions: reward; response cost; and baseline, in which points awarded/deducted for inhibitory performance varied. Results There were effects of diagnosis (CTRL > ADHD unmedicated), medication (on > off), and motivation (reward and/or response cost > baseline) on N2 and P3 amplitude, although the N2 diagnosis effect did not reach statistical significance (p = .1). Interactions between motivation and diagnosis/medication were nonsignificant (p > .1). Conclusions Motivational incentives increased amplitudes of electrophysiological correlates of response conflict and attention in children with ADHD, towards the baseline (low motivation) amplitudes of control subjects. These results suggest that, on these measures, motivational incentives have similar effects in children with ADHD as typically developing CTRLs and have additive effects with stimulant medication, enhancing stimulus salience and allocation of attentional resources during response inhibition.

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.

Autistic spectrum disorder traits in children with attention deficit hyperactivity disorder.

BACKGROUND Current classification systems do not allow for comorbid diagnoses of attention deficit hyperactivity disorder (ADHD) and autistic spectrum disorder (ASD). Children with ADHD are often screened for ASD during clinical assessment and when recruited to clinical trials. We predicted that children with ADHD would have more autistic traits than controls and that certain traits would be more prevalent. METHODS The clinically referred sample consisted of 30 children with ADHD and 30 matched controls aged 9-15 years. Children were screened for ASD traits using the Social Aptitudes Scale (SAS) and the Social Communication Questionnaire (SCQ). RESULTS We found that ASD traits were significantly higher in children with ADHD than controls. None of the children received a diagnosis of autism or ASD. However, a large proportion (28% using the SCQ and 62% using the SAS) of children with ADHD reached screening thresholds for a predictive diagnosis of ASD. Relative to controls, children with ADHD had significantly higher levels of communication and social deficits, but not repetitive behaviours. CONCLUSION Further work is needed to establish whether autistic-like communication and social difficulties in children with ADHD are part of the broader ASD phenotype or are specific to ADHD.

Task induced modulation of neural oscillations in electrophysiological brain networks.

In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms.

Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data

The simultaneous acquisition and subsequent analysis of EEG and fMRI data is challenging owing to increased noise levels in the EEG data. A common method to integrate data from these two modalities is to use aspects of the EEG data, such as the amplitudes of event-related potentials (ERP) or oscillatory EEG activity, to predict fluctuations in the fMRI data. However, this relies on the acquisition of high quality datasets to ensure that only the correlates of neuronal activity are being studied. In this study, we investigate the effects of head-motion-related artefacts in the EEG signal on the predicted T2*-weighted signal variation. We apply our analyses to two independent datasets: 1) four participants were asked to move their feet in the scanner to generate small head movements, and 2) four participants performed an episodic memory task. We created T2*-weighted signal predictors from indicators of abrupt head motion using derivatives of the realignment parameters, from visually detected artefacts in the EEG as well as from three EEG frequency bands (theta, alpha and beta). In both datasets, we found little correlation between the T2*-weighted signal and EEG predictors that were not convolved with the canonical haemodynamic response function (cHRF). However, all convolved EEG predictors strongly correlated with the T2*-weighted signal variation in various regions including the bilateral superior temporal cortex, supplementary motor area, medial parietal cortex and cerebellum. The finding that movement onset spikes in the EEG predict T2*-weighted signal intensity only when the time course of movements is convolved with the cHRF, suggests that the correlated signal might reflect a BOLD response to neural activity associated with head movement. Furthermore, the observation that broad-spectral EEG spikes tend to occur at the same time as abrupt head movements, together with the finding that abrupt movements and EEG spikes show similar correlations with the T2*-weighted signal, indicates that the EEG spikes are produced by abrupt movement and that continuous regressors of EEG oscillations contain motion-related noise even after stringent correction of the EEG data. If not properly removed, these artefacts complicate the use of EEG data as a predictor of T2*-weighted signal variation.

Dissociated large-scale functional connectivity networks of the precuneus in medication-naïve first-episode depression

An imbalance in neural activity within large-scale networks appears to be an important pathophysiological aspect of depression. Yet, there is little consensus regarding the abnormality within the default mode network (DMN) in major depressive disorder (MDD). In the present study, 16 first-episode, medication-naïve patients with MDD and 16 matched healthy controls underwent functional magnetic resonance imaging (fMRI) at rest. With the precuneus (a central node of the DMN) as a seed region, functional connectivity (FC) was measured across the entire brain. The association between the FC of the precuneus and overall symptom severity was assessed using the Hamilton Depression Rating Scale. Patients with MDD exhibited a more negative relationship between the precuneus and the non-DMN regions, including the sensory processing regions (fusiform gyrus, postcentral gyrus) and the secondary motor cortex (supplementary motor area and precentral gyrus). Moreover, greater severity of depression was associated with greater anti-correlation between the precuneus and the temporo-parietal junction as well as stronger positive connectivity between the precuneus and the dorsomedial prefrontal cortex. These results indicate that dissociated large-scale networks of the precuneus may contribute to the clinical expression of depression in MDD.

Looking before you leap: A theory of motivated control of action

The acquisition of volitional control depends, in part, on developing the ability to countermand a planned action. Many tasks have been used to tap the efficiency of this process, but few studies have investigated how it may be modulated by participants’ motivation. Multiple mechanisms may be involved in the deliberate exercise of caution when incentives are provided. For example, control may involve modulation of the efficiency of the countermanding process, and/or inhibitory modulation of the impulse to go. One of the most commonly used paradigms to assess control of action is the Stop Signal Task, in which a primary Go stimulus is occasionally followed by a countermanding Stop signal, allowing a Stop Signal Reaction Time (SSRT) to be inferred as the outcome of a “horse race” between the go and countermanding processes. Here, we present a computational model in which high task motivation modulates proactive pre-stimulus inhibition of the go response. This allows responses to be calibrated so as to fall within a time-window that maximizes the probability of success, regardless of trial type, but does not decrease the observed SSRT. We report empirical support for the model from a sample of typically developing children, and discuss the broader implications for operationalizing measures of volitional control.

Theta power during encoding predicts subsequent‐memory performance and default mode network deactivation

The subsequent memory paradigm, according to which cerebral activity for later remembered (LR) and later forgotten (LF) items is contrasted, can be used to characterize the processes necessary for successful memory encoding. Previous simultaneous electroencephalography/functional magnetic resonance imaging (EEG/fMRI) memory studies suggest an inverse relationship between frontal theta band power and the blood oxygenation level dependent (BOLD) signal in the default mode network (DMN). The principal aim of this EEG/fMRI study was to test the hypothesis that this putative theta‐DMN relationship is less evident in LF compared with LR trials. Fourteen healthy participants performed an episodic memory task in which pictorial stimuli were presented during encoding, and categorized (as LR or LF) by subsequent memory performance. For each encoding trial, the mean of the Hilbert envelope of the theta signal from 400 to 800 ms after stimulus presentation was calculated. To integrate the EEG and fMRI data, general linear models (GLMs) were used to assess the extent to which these single‐trial theta values (as modulators of the main effect of stimulus) predicted DMN BOLD signal change, using: (i) whole‐head univariate GLMs and (ii) GLMs in which the outcome variable was the time‐course of a DMN component derived from spatial independent component analysis of the fMRI data. Theta was significantly greater for LR than LF stimuli. Furthermore, the inverse relationship between theta and BOLD in the DMN was consistently stronger for LR than LF pictures. These findings imply that theta oscillations are key to attenuating processes which may otherwise impair memory encoding. Hum Brain Mapp 34:2929–2943, 2013.