Unraveling Causal Mechanisms of Top-Down and Bottom-Up Visuospatial Attention with Non-invasive Brain Stimulation

General low alertness in people with obstructive sleep apnea

STUDY OBJECTIVES

In patients with obstructive sleep apnea (OSA), a previous study using a Go/No-Go task reported an average attention deficit. However, the temporal dynamics of such a deficit are unknown. Here, we investigated whether attention deficits in different subdomains increased as the test progressed. We also investigated the effect of target frequency and speed of stimulus presentation on performance.

METHODS

Twenty-seven untreated people with OSA and 27 age- and sex-matched controls underwent a 15-minute Go/No-Go task, divided into 6 blocks. Each block was subdivided into 3 different interstimulus intervals (1, 2, and 4 seconds). Three blocks had a low and three had a high target probability (20% and 80%, respectively). Reaction time (alertness), variability of reaction time (sustained attention), commission errors (response inhibition), and omission errors (focused attention) were measured.

RESULTS

Alertness was lower in the group with OSA compared with controls, as evidenced by a significantly higher average reaction time. This effect was seen from the start of the task and continued until the end but did not increase in test progression. The temporal pattern of intrinsic alertness deficits in patients with OSA was found to be independent of target frequency or interstimulus interval.

CONCLUSIONS

The primary attention problem in OSA is on the alertness subdomain irrespective of the number of required responses or speed of stimulus presentation. The present results support the notion that OSA is distinct from other neurological and psychiatric conditions, such as depression or chronic pain. The results also suggest significant concerns regarding daily life activities (eg, driving).

CITATION

de Souza Bezerra ML, van Duinkerken E, Simões E, Schmidt SL. General low alertness in people with obstructive sleep apnea. J Clin Sleep Med. 2024;20(5):689-698.

Development of top-down cortical propagations in youth

Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.

Neural correlates of spatial attention bias: Changes in functional connectivity in attention networks associated with tDCS

The prevailing theory concerning the pathophysiology of unilateral spatial neglect is that it is caused by an interhemispheric imbalance in attention networks. Previous studies have demonstrated that repetitive transcranial magnetic stimulation or transcranial direct current stimulation (tDCS) delivered over the right posterior parietal cortex can induce transitory neglect-like deficits in healthy individuals. We examined whether right cathodal and left anodal tDCS delivered over the posterior parietal cortex could produce neglect-like deficits and change the resting-state functional connectivity (rsFC) of attention networks. We found that the reaction time for targets in the left hemifield was significantly prolonged during two different types of visual search tasks, and rsFC of the attention networks was altered by tDCS. Furthermore, the change in the reaction times for the left visual target in the two different tasks significantly correlated with the change in the rsFC of either the right dorsal attention network (DAN) or right ventral attention network (VAN) based on the tasks. These results suggest that tDCS delivered to the posterior parietal cortex bilaterally induced neglect-like deficits by altering the connectivity of the attentional networks through excitability changes in the cortical area under the electrode. The results of this study are consistent with the hypothesis that the cause of neglect is the interhemispheric imbalance of attention networks. This is the first study to demonstrate that local cortical stimulation can induce changes not only in the local brain function but also in the cortical networks in healthy individuals.

A Midbrain Inspired Recurrent Neural Network Model for Robust Change Detection

We present a biologically inspired recurrent neural network (RNN) that efficiently detects changes in natural images. The model features sparse, topographic connectivity (st-RNN), closely modeled on the circuit architecture of a "midbrain attention network." We deployed the st-RNN in a challenging change blindness task, in which changes must be detected in a discontinuous sequence of images. Compared with a conventional RNN, the st-RNN learned 9x faster and achieved state-of-the-art performance with 15x fewer connections. An analysis of low-dimensional dynamics revealed putative circuit mechanisms, including a critical role for a global inhibitory (GI) motif, for successful change detection. The model reproduced key experimental phenomena, including midbrain neurons' sensitivity to dynamic stimuli, neural signatures of stimulus competition, as well as hallmark behavioral effects of midbrain microstimulation. Finally, the model accurately predicted human gaze fixations in a change blindness experiment, surpassing state-of-the-art saliency-based methods. The st-RNN provides a novel deep learning model for linking neural computations underlying change detection with psychophysical mechanisms.SIGNIFICANCE STATEMENT For adaptive survival, our brains must be able to accurately and rapidly detect changing aspects of our visual world. We present a novel deep learning model, a sparse, topographic recurrent neural network (st-RNN), that mimics the neuroanatomy of an evolutionarily conserved "midbrain attention network." The st-RNN achieved robust change detection in challenging change blindness tasks, outperforming conventional RNN architectures. The model also reproduced hallmark experimental phenomena, both neural and behavioral, reported in seminal midbrain studies. Lastly, the st-RNN outperformed state-of-the-art models at predicting human gaze fixations in a laboratory change blindness experiment. Our deep learning model may provide important clues about key mechanisms by which the brain efficiently detects changes.

Scalp-recorded N40 visual evoked potential: Sensory and attentional properties

N40 is a well-known component of evoked potentials with respect to the auditory and somatosensory modality but not much recognized with regard to the visual modality. To be detected with event-related potentials (ERPs), it requires an optimal signal-to-noise ratio. To investigate the nature of visual N40, we recorded EEG/ERP signals from 20 participants. Each of them was presented with 1800 spatial frequency gratings of 0.75, 1.5, 3 and 6 c/deg. Data were collected from 128 sites while participants were engaged in both passive viewing and attention conditions. N40 (30-55 ms) was modulated by alertness and selective attention; in fact, it was larger to targets than irrelevant and passively viewed spatial frequency gratings. Its strongest intracranial sources were the bilateral thalamic nuclei of pulvinar, according to swLORETA. The active network included precuneus, insula and inferior parietal lobule. An N80 component (60-90 ms) was also identified, which was larger to targets than irrelevant/passive stimuli and more negative to high than low spatial frequencies. In contrast, N40 was not sensitive to spatial frequency per se, nor did it show a polarity inversion as a function of spatial frequency. Attention, alertness and spatial frequency effects were also found for the later components P1, N2 and P300. The attentional effects increased in magnitude over time. The data showed that ERPs can pick up the earliest synchronized activity, deriving in part from thalamic nuclei, before the visual information has actually reached the occipital cortex.

A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations

Researchers in neuroscience computing experience difficulties when they try to carry out neuroanalysis in practice or when they need to design an explainable brain-computer interface (BCI) with quick setup and minimal training phase. There is a need of interpretable computational intelligence techniques and new brain states decoding for more understandable interpretation of the sensory, cognitive, and motor brain processing. We propose a general-purpose fuzzy software system shell for developing a custom EEG BCI system. It relies on the bursts of the ongoing EEG frequency power synchronization/desynchronization at scalp level and supports quick BCI setup by linguistic features, ad hoc fuzzy membership construction, explainable IF-THEN rules, and the concept of the Internet of Things (IoT), which makes the BCI system device and service independent. It has a potential for designing both passive and event-related BCIs with options for visual representation at scalp-source level in response to time. The feasibility of the proposed system has been proven by real experiments and bursts for β and γ frequency power have been detected in real time in response to evoked visuospatial selective attention. The presence of the proposed new brain state decoding can be used as a feasible metric for interpretation of the spatiotemporal dynamics of the passive or evoked neural oscillations.

FEF Excitability in Attentional Bias: A TMS-EEG Study

The role of distinct cortical regions in guiding social orienting needs further investigation. Our aim was to explore the contribution of the frontal eye field (FEF) in early orienting of attention towards stimuli with social value. We used a TMS-EEG approach to investigate event related potentials (ERPs; no-TMS block) and TMS evoked potentials (TEPs; TMS block) during the cueing phase of a modified version of the dot-probe task, comparing competing (face vs. house) and not competing (house vs. house) conditions. Our results revealed an increased amplitude of ERP components in the competing condition, showing greater posterior N170 and fronto-central vertex positive potential (VPP) and an enhanced frontal negative component at 250-270 ms from cue onset. TMS pulses over the FEF induced similar N170 and VPP amplified components. In addition, in the ERPs, a reduced positivity at 400 ms was shown when the face appeared on the left side vs. the right side of space. In contrast, in the TMS blocks, we found lateralized effects on N170 depending on the side of face presentation. The enhanced cortical excitability induced by TMS over the right FEF significantly correlated with the performance on the behavioral task, suggesting a link between the FEF activity during the cueing phase of the dot-probe task and the subsequent behavioral response times to the targets.