Attention--the ability to attend to some things while ignoring others - can be best described as an emergent property of many neural mechanisms, facilitatory and inhibitory, working together to resolve competition for processing resources and control of behavior. Previous EEG and MEG studies examining the neural mechanisms underlying facilitation and inhibition of stimulus processing typically used paradigms requiring alternating shifts of attention in the spatial domain, with stimuli occurring at both attended and unattended locations. These studies generally observed greater pre-stimulus alpha oscillations over task-irrelevant vs. relevant posterior regions and bilateral attentional modulations of early sensory processing. In contrast, in the current series of experiments, participants continuously attended to only one hemifield and stimuli were only presented at the attended location, affording us an opportunity to elucidate the inhibitory and facilitatory effects of attention in the brain in a context in which spatial relevance was fixed. We found that continuous attention to one hemifield did not modulate prestimulus alpha activity in ipsilateral regions but did result in a perfectly lateralized P1 attention effect to ipsilateral posterior regions. Moreover, we found a bilateral N1 effect. These findings suggest that pre-stimulus alpha activity, the P1 and the N1 reflect qualitatively different aspects of attention; While pre-stimulus alpha-band activity may reflect a top-down inhibitory mechanism that critically depends on functional competition between task-relevant and irrelevant sensory regions, the ipsilateral P1 effect may reflect stimulus-triggered blocking of sensory processing in irrelevant networks, and the N1 effect facilitation of task-relevant processing.

Predictive coding models propose that predictions (stimulus likelihood) reduce sensory signals as early as primary visual cortex (V1), and that attention (stimulus relevance) can modulate these effects. Indeed, both prediction and attention have been shown to modulate V1 activity, albeit with fMRI, which has low temporal resolution. This leaves it unclear whether these effects reflect a modulation of the first feedforward sweep of visual information processing and/or later, feedback-related activity. In two experiments, we used electroencephalography and orthogonally manipulated spatial predictions and attention to address this issue. Although clear top-down biases were found, as reflected in pre-stimulus alpha-band activity, we found no evidence for top-down effects on the earliest visual cortical processing stage (<80 ms post-stimulus), as indexed by the amplitude of the C1 event-related potential component and multivariate pattern analyses. These findings indicate that initial visual afferent activity may be impenetrable to top-down influences by spatial prediction and attention.

The brain is limited in its capacity to consciously process information, necessitating gating of information. While conscious perception is robustly associated with sustained, recurrent interactions between widespread cortical regions, subcortical regions, including the striatum, influence cortical activity. Here, we examined whether the ventral striatum, given its ability to modulate cortical information flow, contributes to conscious perception. Using intracranial EEG, we recorded ventral striatum activity while 7 patients performed an attentional blink task in which they had to detect two targets (T1 and T2) in a stream of distractors. Typically, when T2 follows T1 within 100 -500 ms, it is often not perceived (i.e., the attentional blink). We found that conscious T2 perception was influenced and signaled by ventral striatal activity. Specifically, the failure to perceive T2 was foreshadowed by a T1-induced increase in ␣ and low ␤ oscillatory activity as early as 80 ms after T1, indicating that the attentional blink to T2 may be due to very early T1-driven attentional capture. Moreover, only consciously perceived targets were associated with an increase in activity between 200 and 400 ms. These unique findings shed new light on the mechanisms that give rise to the attentional blink by revealing that conscious target perception may be determined by T1 processing at a much earlier processing stage than traditionally believed. More generally, they indicate that ventral striatum activity may contribute to conscious perception, presumably by gating cortical information flow.

Our ability to stay focused is limited: prolonged performance of a task typically results in mental fatigue and decrements in performance over time. This so-called vigilance decrement has been attributed to depletion of attentional resources, though other factors such as reductions in motivation likely also play a role. In this study, we examined three EEG markers of attentional control, to elucidate which stage of attentional processing is most affected by time-on-task and motivation. To elicit the vigilance decrement, participants performed a sustained attention task for 80 minutes without breaks. After 60 minutes, participants were motivated by an unexpected monetary incentive to increase performance in the final 20 minutes. We found that task performance and self-reported motivation declined rapidly, reaching a stable levels well before the motivation manipulation was introduced. Thereafter, motivation increased back up to the initial level, and remained there for the final 20 minutes. While task performance also increased, it did not return to the initial level, and fell to the lowest level overall during the final 10 minutes. This pattern of performance changes was mirrored by the trial-to-trial consistency of the phase of theta (3-7 Hz) oscillations, an index of the variability in timing of the neural response to the stimulus. As task performance decreased, temporal variability increased, suggesting that attentional stability is crucial for sustained attention performance. The effects of attention on our two other EEG measures-early P1/N1 event-related potentials and pre-stimulus alpha (9-14 Hz) power-did not change with time-on-task or motivation. In sum, these findings show that the vigilance decrement is accompanied by a decline in only some facets of attentional control, which cannot be fully brought back online by increases in motivation. The vigilance decrement might thus not occur due to a single cause, but is likely multifactorial in origin.

Attention is a fundamental cognitive process-without it, we would be helplessly adrift in an overload of sensory input. There is considerable interest in techniques that can be used to enhance attention, including transcranial electrical stimulation (tES). We present an overview of 52 studies that have paired attention tasks with tES, mostly in the form of transcranial direct current stimulation (tDCS). In particular, we discuss four aspects of attention that have been most extensively targeted to date: visual search, spatial orienting (e.g., Posner cueing tasks), spatial bias (e.g., line bisection tasks), and sustained attention. Some promising results have been reported in each of these domains. However, drawing general conclusions about the efficacy of tES is at present hampered by a large diversity in study design and inconsistent findings. We highlight some pitfalls and opportunities and suggest how these may be addressed in future research aiming to use tES as a tool to enhance or test theoretical hypotheses about attention.

Influential theories propose that sensory predictions based on regularities in the environment influence information processing across the cortical hierarchy, and that attention may regulate these effects. At present, it is unclear if predictions can modulate the first feedforward sweep of visual information processing, and how this depends on attention. To address this outstanding issue, we orthogonally manipulated visuospatial predictions and attention, and used EEG and a design optimized to measure activity generated by primary visual cortex. Evidence was only found for later (>80ms) top-down modulation of cortical activity. These results have important theoretical implications in that they suggest, together with previous attention studies, that the very first stage of cortical visual information processing may generally be impenetrable to top-down influences.2009). Crucially, as prediction and attention may have opposite effects on sensory processing, with predictions reducing sensory responses and attention boosting sensory responses, their effects on visual processing may cancel out when not properly controlled for in experimental designs, potentially explaining the absence of C1 modulations in previous studies. Thus, at present, it is unclear whether predictions and attention can modulate the first feedforward sweep of cortical visual information processing, and if so, how.Here, we orthogonally manipulated prediction (stimulus location predictability) and attention (relevance), and exploited the high temporal resolution of EEG, to test this issue. Next to examining modulations of the C1, we used multivariate pattern analysis (MVPA) to further investigate how early prediction and attention may modulate visual representations. Lastly, we also explored their effects on pre-stimulus activity, indicative of a top-down bias, and on several later ERP components that capture subsequent processing stages.

In a previous study using transcranial alternating current stimulation (tACS), we found preliminary evidence that phase coherence in the alpha band (8–12 Hz) within the fronto-parietal network may critically support top-down control of spatial attention (van Schouwenburg et al., 2017). Specifically, synchronous alpha-band stimulation over the right frontal and parietal cortex (0° relative phase) was associated with changes in performance and fronto-parietal coherence during a spatial attention task as compared to sham stimulation. In the current study, we firstly aimed to replicate these findings with synchronous tACS. Second, we extended our previous protocol by adding a second tACS condition in which the right frontal and parietal cortex were stimulated in a desynchronous fashion (180° relative phase), to test the specificity of the changes observed in our previous study. Participants (n = 23) were tested in three different sessions in which they received either synchronous, desynchronous, or sham stimulation over the right frontal and parietal cortex. In contrast to our previous study, we found no spatially selective effects of stimulation on behavior or coherence in either stimulation protocol compared to sham. We highlight some of the differences in study design that may have contributed to this discrepancy in findings and more generally may determine the effectiveness of tACS.

Background The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data. Methods Routine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data. Results We identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82–0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71–0.75)). Conclusions During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research.