The Theory of Visual Attention (TVA) provides a mathematical formalisation of the "biased competition" account of visual attention. Applying this model to individual performance in a free recall task allows the estimation of 5 independent attentional parameters: visual short-term memory (VSTM) capacity, speed of information processing, perceptual threshold of visual detection; attentional weights representing spatial distribution of attention (spatial bias), and the top-down selectivity index. While the TVA focuses on selection in space, complementary accounts of attention describe how attention is maintained over time, and how temporal processes interact with selection. A growing body of evidence indicates that different facets of attention interact and share common neural substrates. The aim of the current study was to modulate a spatial attentional bias via transfer effects, based on a mechanistic understanding of the interplay between spatial, selective and temporal aspects of attention. Specifically, we examined here: (i) whether a single administration of a lateralized sustained attention task could prime spatial orienting and lead to transferable changes in attentional weights (assigned to the left vs right hemi-field) and/or other attentional parameters assessed within the framework of TVA (Experiment 1); (ii) whether the effects of such spatial-priming on TVA parameters could be further enhanced by bi-parietal high frequency transcranial random noise stimulation (tRNS) (Experiment 2). Our results demonstrate that spatial attentional bias, as assessed within the TVA framework, was primed by sustaining attention towards the right hemi-field, but this spatial-priming effect did not occur when sustaining attention towards the left. Furthermore, we show that bi-parietal high-frequency tRNS combined with the rightward spatial-priming resulted in an increased attentional selectivity. To conclude, we present a novel, theory-driven method for attentional modulation providing important insights into how the spatial and temporal processes in attention interact with attentional selection.

Intermittent theta burst stimulation (iTBS) delivered to the dorsolateral prefrontal cortex (DLPFC) has been investigated as a promising treatment for stress and stressrelated mental disorders such as major depression, yet large individual differences in responsiveness demand further exploration and optimization of its effectiveness. Clinical research suggests that resting-state functional connectivity (rsFC) between the DLPFC and the anterior cingulate cortex (ACC) can predict iTBS treatment response in depression. The present study aimed to investigate whether rsFC between the left DLPFC and ACC subregions could predict the degree to which the stress system is affected by iTBS. After assessment of baseline resting-state fMRI data, 34 healthy female participants performed the Trier Social Stress Test on two separate days, each followed by active or sham iTBS over the left DLPFC. To evaluate iTBS effects on the stress-system, salivary cortisol was measured throughout the procedure. Our results showed that a stronger negative correlation between the left DLPFC and the caudal ACC was linked to a larger attenuation of stress-system sensitivity during active, but not during sham iTBS. In conclusion, based on individual rsFC between left DLPFC and caudal ACC, iTBS could be optimized to more effectively attenuate deregulation of the stress system.

One of the main challenges in fMRI processing is filtering the task BOLD signals from the noise. Independent component analysis with automatic removal of motion artifacts (ICA-AROMA) reduces motion artifacts by identifying ICA noise components based on their location at the brain edges and cerebrospinal fluid (CSF), high frequency content and correlation with motion regressors. In anatomical component correction (aCompCor), physiological noise regressors extracted from CSF were regressed out from the fMRI time series. In this study, we compared three methods to combine aCompCor and ICA-AROMA denoising in one denoising step. In the first analysis, we regressed the temporal signals of the ICA components identified as noise by ICA-AROMA together with the noise signals determined by aCompCor from the fMRI signals. For the second and third analyses, the correlation between the temporal signals of the ICA components and the aCompCor noise signals was used as an additional criterion to identify the noise components. In the second analysis, the temporal signals of the ICA components classified as noise were regressed from the fMRI signals. In the third analysis, the noise components were removed. To compare the denoising strategies, we examined the fractional amplitude of low-frequency fluctuations (fALFF) and the overlap between the contrast maps. Our results revealed that including the aCompCor noise signals as regressors in ICA-AROMA resulted in more correctly identified noise components, higher fALFF values, and larger activation maps. Moreover, combining the temporal signals of the noise components identified by ICA-AROMA with the aCompCor signals in a noise regression matrix resulted in deactivations. These results suggest that using the correlation between the ICA component temporal signals and the aCompCor signals as noise identification criteria in ICA-AROMA is the best approach for combining both denoising methods.

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