Experimental pre-tests of public health communications on the COVID-19 vaccine: A null finding for medical endorsement, risk and altruism.

OBJECTIVE: Vaccination campaigns against COVID-19 will only be successful if enough people want to take the vaccine. We tested a government communications intervention to encourage uptake. DESIGN: A pre-registered randomised controlled trial. METHODS: A large, nationally representative sample were randomly assigned to see one of eight posters. The posters varied by image (general practitioner or two hospital doctors) and message (control with public health guidance not related to vaccination, endorsement of the vaccine from the pictured doctor, endorsement with information about COVID-19 risk, endorsement with information about risk and appeal to get vaccinated to protect friends and family). The posters were presented as part of a larger study. The main outcomes were intention to be vaccinated and how soon people would be willing to be vaccinated. RESULTS: The posters induced different reactions indicating that participants had engaged with them. The hospital image was generally preferred to the GP image. Perhaps critically, all intervention messages were trusted less than a control message which did not mention the vaccine (Control Poster Mean = 5.65, SE = 0.09 vs. Poster M Mean = 5.18, SE = 0.09, p <.001; vs. Poster M + R Mean = 5.11, SE = 0.09, p <.001; vs. Poster M + R + F Mean = 5.33, SE = 0.09, p =.01). There were no effects of poster type on intention to take the vaccine or how soon people were willing to take it. CONCLUSION: Although the intervention messages were based on the strongest correlates of vaccine hesitancy identified by contemporaneous surveys, none was effective. More recent research suggests that focusing on the risk of COVID-19 may be less effective than focusing on the benefits of vaccination. Null findings can be as important as positive findings for designing public health campaigns. This study informed government communications about the COVID-19 vaccine.

Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (ß-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.

Modulation of the Earliest Component of the Human VEP by Spatial Attention: An Investigation of Task Demands.

Spatial attention modulations of initial afferent activity in area V1, indexed by the first component "C1" of the human visual evoked potential, are rarely found. It has thus been suggested that early modulation is induced only by special task conditions, but what these conditions are remains unknown. Recent failed replications-findings of no C1 modulation using a certain task that had previously produced robust modulations-present a strong basis for examining this question. We ran 3 experiments, the first to more exactly replicate the stimulus and behavioral conditions of the original task, and the second and third to manipulate 2 key factors that differed in the failed replication studies: the provision of informative performance feedback, and the degree to which the probed stimulus features matched those facilitating target perception. Although there was an overall significant C1 modulation of 11%, individually, only experiments 1 and 2 showed reliable effects, underlining that the modulations do occur but not consistently. Better feedback induced greater P1, but not C1, modulations. Target-probe feature matching had an inconsistent influence on modulation patterns, with behavioral performance differences and signal-overlap analyses suggesting interference from extrastriate modulations as a potential cause.

Patterned functional network disruption in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting-state electroencephalography recordings from 74 ALS patients and 47 age-matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co-modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ- to ß-band), lateral/orbitofrontal (δ- to θ-band) and sensorimotor (ß-band) regions of the brain in patients with ALS. Furthermore, we show increased co-modulation of neural oscillations in the central and posterior (δ-, θ- and γl -band) and frontal (δ- and γl -band) regions, as well as decreased synchrony in the temporal and frontal (δ- to ß-band) and sensorimotor (ß-band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease-associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.

Dysfunction of attention switching networks in amyotrophic lateral sclerosis.

OBJECTIVE: To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms. RATIONALE: The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigated as a quantitative biomarker of impairment in ALS or its sub-phenotypes. METHODS: MMN responses from 128-channel electroencephalography (EEG) recordings in 58 ALS patients and 39 healthy controls were localised to source by three separate localisation methods, including beamforming, dipole fitting and exact low resolution brain electromagnetic tomography. RESULTS: Compared with controls, ALS patients showed significant increase in power of the left posterior parietal, central and dorsolateral prefrontal cortices (false discovery rate = 0.1). This change correlated with impaired cognitive flexibility (rho = 0.45, 0.45, 0.47, p = .042, .055, .031 respectively). ALS patients also exhibited a decrease in the power of dipoles representing activity in the inferior frontal (left: p = 5.16 × 10-6, right: p = 1.07 × 10-5) and left superior temporal gyri (p = 9.30 × 10-6). These patterns were detected across three source localisation methods. Decrease in right inferior frontal gyrus activity was a good discriminator of ALS patients from controls (AUROC = 0.77) and an excellent discriminator of C9ORF72 expansion-positive patients from controls (AUROC = 0.95). INTERPRETATION: Source localization of evoked potentials can reliably discriminate patterns of functional network impairment in ALS and ALS subgroups during involuntary attention switching. The discriminative ability of the detected cognitive changes in specific brain regions are comparable to those of functional magnetic resonance imaging (fMRI). Source analysis of high-density EEG patterns has excellent potential to provide non-invasive, data-driven quantitative biomarkers of network disruption that could be harnessed as novel neurophysiology-based outcome measures in clinical trials.

Characteristic Increases in EEG Connectivity Correlate With Changes of Structural MRI in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a terminal progressive adult-onset neurodegeneration of the motor system. Although originally considered a pure motor degeneration, there is increasing evidence of disease heterogeneity with varying degrees of extra-motor involvement. How the combined motor and nonmotor degeneration occurs in the context of broader disruption in neural communication across brain networks has not been well characterized. Here, we have performed high-density crossectional and longitudinal resting-state electroencephalography (EEG) recordings on 100 ALS patients and 34 matched controls, and have identified characteristic patterns of altered EEG connectivity that have persisted in longitudinal analyses. These include strongly increased EEG coherence between parietal-frontal scalp regions (in γ-band) and between bilateral regions over motor areas (in θ-band). Correlation with structural MRI from the same patients shows that disease-specific structural degeneration in motor areas and corticospinal tracts parallels a decrease in neural activity over scalp motor areas, while the EEG over the scalp regions associated with less extensively involved extra-motor regions on MRI exhibit significantly increased neural communication. Our findings demonstrate that EEG-based connectivity mapping can provide novel insights into progressive network decline in ALS. These data pave the way for development of validated cost-effective spectral EEG-based biomarkers that parallel changes in structural imaging.

Task dependence of early attention modulation: the plot thickens.

In Kelly, Gomez-Ramirez and Foxe (2008), we demonstrated strong spatial attentional modulation of initial afferent activity in human area V1 reflected in the C1 visual evoked potential (VEP) component. Using the same task and analysis strategy, Baumgartner and colleagues provide compelling evidence that there is no such modulation in their data. Here, we examine differences in task conditions between this new study and our original study, speculate on how they may account for the discrepant findings, and discuss the broader theoretical implications.

The spatiotemporal characteristics of the C1 component and its modulation by attention.

Slotnick (this issue) provided a selective review of studies on the attentional modulation of the C1 component of the visual evoked potential, and offers a number of guidelines to maximize the likelihood of observing such modulation in terms of electrode choice, stimulus placement, and types of attentional cue and target stimulus. However, the broader literature pertaining to attentional modulation of the C1 does not support many of these guidelines, and the question of why exactly C1 modulations are so rare remains very much open. Here, we provide clarifications that are critical to an accurate appraisal of the current state of this literature.

Estimation of coherence using the median is robust against EEG artefacts.

Coherence is a mathematical measure of correlation in the frequency domain, commonly used to quantify the oscillatory synchrony of bio-signals such as the electroencephalogram (EEG). In biomedical applications, such as assessment of functional connectivity, reliable estimation of coherence is of paramount importance for studying the function of complex brain networks, as well as their disruption in neurological disorders. A major challenge for robust estimation of coherence measures is the presence of artefacts. Here, we propose an alternative method for finding coherence by estimating auto- and cross-spectral densities based on the median or trimmed-mean values across trials, rather than the mean. The variance of the average cortico-cortical coherence measures, i.e., the inter-individual variability, was taken as a measure of robustness and tested on resting-state recordings from 34 healthy individuals, both without screening, as well as after screening by a statistical thresholding artefact rejection. The variability of average coherence in individual channels and frequency bands decreased by using the median-based estimation of coherence. Averaged across all channels and frequency bands, the variability of coherence estimates based on median was significantly lower than mean-based estimates for both unscreened data (F = 9.28, p = 0.003, 1-ß0.05 = 0.98) and screened data (F = 6.58, p = 0.01, 1-ß0.05 = 0.91). Moreover, the variability for median-based estimates was almost identical for unscreened and screened data (F = 0.004, p = 0.95), suggesting that coherence based on median without artefact rejection might be sufficient for robust estimation of coherence.

Mismatch Negativity as an Indicator of Cognitive Sub-Domain Dysfunction in Amyotrophic Lateral Sclerosis.

OBJECTIVE: To evaluate the utility of mismatch negativity (MMN), a neurophysiologic marker of non-motor cognitive processing, in amyotrophic lateral sclerosis (ALS). METHODS: 89 patients, stratified into 4 different phenotypic presentations of ALS (67 spinal-onset, 15 bulbar-onset, 7 ALS-FTD, 7 C9ORF72 gene careers), and 19 matched controls underwent 128-channel EEG data recording. Subjects were presented with standard auditory tones interleaved with pitch-deviant tones in three recording blocks. The MMN response was quantified by peak amplitude, peak delay, average amplitude, and average delay, 100-300 ms after stimuli. 64 patients underwent cognitive screening using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS), and 38 participants underwent contemporaneous cognitive assessment using the Stroop Color-Word Interference test (CWIT), which measures attention shift, inhibitory control, and error monitoring. RESULTS: The MMN response was observed in frontal and frontocentral regions of patient and control groups. Compared to controls, waveforms were attenuated in early onset, and the average delay was significantly increased in all of the ALS subgroups, with no significant difference between subgroups. Comparing with the control response, the ALS MMN response clustered into four new subgroups characterized by differences in response latency. The increased average delay correlated with changes in the Stroop CWIT; however, it did not show a direct relationship with age, gender, traditional phenotypes, revised ALS Functional Rating Scale, or ECAS scores. CONCLUSION AND SIGNIFICANCE: The MMN response in ALS patients reflects the cognitive dysfunction in specific sub-domains, as the new patient subgroups, identified by cluster analysis, do not segregate with existing clinical or cognitive classifications. Event-related potentials can provide additional quantitative neurophysiologic measures of impairment in specific cognitive sub-domains from which it may be possible to generate novel biologically relevant subgroups of ALS.

Predicting explorative motor learning using decision-making and motor noise.

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.