Perceptual organization and task demands jointly shape auditory working memory capacity

Listeners performed two different tasks in which they remembered short sequences comprising either complex tones (generally heard as one melody) or everyday sounds (generally heard as separate objects). In one, listeners judged whether a probe item had been present in the preceding sequence. In the other, they judged whether a second sequence of the same items was identical in order to the preceding sequence. Performance on the first task was higher for everyday sounds; performance on the second was higher for complex tones. Perceptual organization strongly shapes listeners' memory for sounds, with implications for real-world communication.

Top-down auditory attention modulates neural responses more strongly in neurotypical than ADHD young adults

Human cognitive abilities naturally vary along a spectrum, even among those we call "neurotypical". Individuals differ in their ability to selectively attend to goal-relevant auditory stimuli. We sought to characterize this variability in a cohort of people with diverse attentional functioning. We recruited both neurotypical (N = 20) and ADHD (N = 25) young adults, all with normal hearing. Participants listened to one of three concurrent, spatially separated speech streams and reported the order of the syllables in that stream while we recorded electroencephalography (EEG). We tested both the ability to sustain attentional focus on a single "Target" stream and the ability to monitor the Target but flexibly either ignore or switch attention to an unpredictable "Interrupter" stream from another direction that sometimes appeared. Although differences in both stimulus structure and task demands affected behavioral performance, ADHD status did not. In both groups, the Interrupter evoked larger neural responses when it was to be attended compared to when it was irrelevant, including for the P3a "reorienting" response previously described as involuntary. This attentional modulation was weaker in ADHD listeners, even though their behavioral performance was the same. Across the entire cohort, individual performance correlated with the degree of top-down modulation of neural responses. These results demonstrate that listeners differ in their ability to modulate neural representations of sound based on task goals, while suggesting that adults with ADHD may have weaker volitional control of attentional processes than their neurotypical counterparts.

Defining attention from an auditory perspective

Attention prioritizes certain information at the expense of other information in ways that are similar across vision, audition, and other sensory modalities. It influences how-and even what-information is represented and processed, affecting brain activity at every level. Much of the core research into cognitive and neural mechanisms of attention has used visual tasks. However, the same top-down, object-based, and bottom-up attentional processes shape auditory perception, largely through the same underlying, cognitive networks. This article is categorized under: Psychology > Attention.

Decoding auditory attention from EEG using a convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2021;2021:6586-6589. [PMID: 34892618 DOI: 10.1109/embc46164.2021.9630484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Brain-computer interface (BCI) systems allow users to communicate directly with a device using their brain. BCI devices leveraging electroencephalography (EEG) signals as a means of communication typically use manual feature engineering on the data to perform decoding. This approach is time intensive, requires substantial domain knowledge, and does not translate well, even to similar tasks. To combat this issue, we designed a convolutional neural network (CNN) model to perform decoding on EEG data collected from an auditory attention paradigm. Our CNN model not only bypasses the need for manual feature engineering, but additionally improves decoding accuracy (∼77%) and efficiency (∼11 bits/min) compared to a support vector machine (SVM) baseline. The results demonstrate the potential for the use of CNN in auditory BCI designs.

Extended Frontal Networks for Visual and Auditory Working Memory

Working memory (WM) supports the persistent representation of transient sensory information. Visual and auditory stimuli place different demands on WM and recruit different brain networks. Separate auditory- and visual-biased WM networks extend into the frontal lobes, but several challenges confront attempts to parcellate human frontal cortex, including fine-grained organization and between-subject variability. Here, we use differential intrinsic functional connectivity from 2 visual-biased and 2 auditory-biased frontal structures to identify additional candidate sensory-biased regions in frontal cortex. We then examine direct contrasts of task functional magnetic resonance imaging during visual versus auditory 2-back WM to validate those candidate regions. Three visual-biased and 5 auditory-biased regions are robustly activated bilaterally in the frontal lobes of individual subjects (N = 14, 7 women). These regions exhibit a sensory preference during passive exposure to task stimuli, and that preference is stronger during WM. Hierarchical clustering analysis of intrinsic connectivity among novel and previously identified bilateral sensory-biased regions confirms that they functionally segregate into visual and auditory networks, even though the networks are anatomically interdigitated. We also observe that the frontotemporal auditory WM network is highly selective and exhibits strong functional connectivity to structures serving non-WM functions, while the frontoparietal visual WM network hierarchically merges into the multiple-demand cognitive system.

Individual Subject Approaches to Mapping Sensory-Biased and Multiple-Demand Regions in Human Frontal Cortex

Sensory modality, widely accepted as a key factor in the functional organization of posterior cortical areas, also shapes the organization of human frontal lobes. 'Deep imaging,' or the practice of collecting a sizable amount of data on individual subjects, offers significant advantages in revealing fine-scale aspects of functional organization of the human brain. Here, we review deep imaging approaches to mapping multiple sensory-biased and multiple-demand regions within human lateral frontal cortex. In addition, we discuss how deep imaging methods can be transferred to large public data sets to further extend functional mapping at the group level. We also review how 'connectome fingerprinting' approaches, combined with deep imaging, can be used to localize fine-grained functional organization in individual subjects using resting-state data. Finally, we summarize current 'best practices' for deep imaging.

Decoding auditory attention from single-trial EEG for a high-efficiency brain-computer interface

Brain-computer interface (BCI) systems enable humans to communicate with a machine in a non-verbal and covert way. Many past BCI designs used visual stimuli, due to the robustness of neural signatures evoked by visual input. However, these BCI systems can only be used when visual attention is available. This study proposes a new BCI design using auditory stimuli, decoding spatial attention from electroencephalography (EEG). Results show that this new approach can decode attention with a high accuracy (>75%) and has a high information transfer rate (>10 bits/min) compared to other auditory BCI systems. It also has the potential to allow decoding that does not depend on subject-specific training.

Audio-visual spatial alignment improves integration in the presence of a competing audio-visual stimulus

In order to parse the world around us, we must constantly determine which sensory inputs arise from the same physical source and should therefore be perceptually integrated. Temporal coherence between auditory and visual stimuli drives audio-visual (AV) integration, but the role played by AV spatial alignment is less well understood. Here, we manipulated AV spatial alignment and collected electroencephalography (EEG) data while human subjects performed a free-field variant of the "pip and pop" AV search task. In this paradigm, visual search is aided by a spatially uninformative auditory tone, the onsets of which are synchronized to changes in the visual target. In Experiment 1, tones were either spatially aligned or spatially misaligned with the visual display. Regardless of AV spatial alignment, we replicated the key pip and pop result of improved AV search times. Mirroring the behavioral results, we found an enhancement of early event-related potentials (ERPs), particularly the auditory N1 component, in both AV conditions. We demonstrate that both top-down and bottom-up attention contribute to these N1 enhancements. In Experiment 2, we tested whether spatial alignment influences AV integration in a more challenging context with competing multisensory stimuli. An AV foil was added that visually resembled the target and was synchronized to its own stream of synchronous tones. The visual components of the AV target and AV foil occurred in opposite hemifields; the two auditory components were also in opposite hemifields and were either spatially aligned or spatially misaligned with the visual components to which they were synchronized. Search was fastest when the auditory and visual components of the AV target (and the foil) were spatially aligned. Attention modulated ERPs in both spatial conditions, but importantly, the scalp topography of early evoked responses shifted only when stimulus components were spatially aligned, signaling the recruitment of different neural generators likely related to multisensory integration. These results suggest that AV integration depends on AV spatial alignment when stimuli in both modalities compete for selective integration, a common scenario in real-world perception.

Gradients of functional organization in posterior parietal cortex revealed by visual attention, visual short-term memory, and intrinsic functional connectivity

Visual attention and visual working memory tasks recruit a common network of lateral frontal cortical (LFC) and posterior parietal cortical (PPC) regions. Here, we examine finer-scale organization of this frontoparietal network. Three LFC regions recruited by visual cognition tasks, superior precentral sulcus (sPCS), inferior precentral sulcus (iPCS), and mid inferior frontal sulcus (midIFS) exhibit differential patterns of resting-state functional connectivity to PPC. A broad dorsomedial to ventrolateral gradient is observed, with sPCS connectivity dominating in the dorsomedial PPC band, iPCS dominating in the middle band, and midIFS dominating in the ventrolateral band. These connectivity-defined subregions of PPC capture differential task activation between a pair of visual attention and working memory tasks. The relative functional connectivity of sPCS and iPCS also varies along the rostral-caudal axis of the retinotopic regions of PPC. iPCS connectivity is relatively stronger near the IPS0/IPS1 and IPS2/IPS3 borders, especially on the lateral portions of these borders, which each preferentially encode central visual field representations. In contrast, sPCS connectivity is relatively stronger elsewhere in retinotopic IPS regions which preferentially encode peripheral visual field representations. These findings reveal fine-scale gradients in functional connectivity within the frontoparietal visual network that capture a high-degree of specificity in PPC functional organization.

Effects of Visual Scene Complexity on Neural Signatures of Spatial Attention

Spatial selective attention greatly affects our processing of complex visual scenes, yet the way in which the brain selects relevant objects while suppressing irrelevant objects is still unclear. Evidence of these processes has been found using non-invasive electroencephalography (EEG). However, few studies have characterized these measures during attention to dynamic stimuli, and little is known regarding how these measures change with increased scene complexity. Here, we compared attentional modulation of the EEG N1 and alpha power (oscillations between 8–14 Hz) across three visual selective attention tasks. The tasks differed in the number of irrelevant stimuli presented, but all required sustained attention to the orientation trajectory of a lateralized stimulus. In scenes with few irrelevant stimuli, top-down control of spatial attention is associated with strong modulation of both the N1 and alpha power across parietal-occipital channels. In scenes with many irrelevant stimuli in both hemifields, however, top-down control is no longer represented by strong modulation of alpha power, and N1 amplitudes are overall weaker. These results suggest that as a scene becomes more complex, requiring suppression in both hemifields, the neural signatures of top-down control degrade, likely reflecting some limitation in EEG to represent this suppression.

Prediction of individualized task activation in sensory modality-selective frontal cortex with 'connectome fingerprinting'

The human cerebral cortex is estimated to comprise 200-300 distinct functional regions per hemisphere. Identification of the precise anatomical location of an individual's unique set of functional regions is a challenge for neuroscience that has broad scientific and clinical utility. Recent studies have demonstrated the existence of four interleaved regions in lateral frontal cortex (LFC) that are part of broader visual attention and auditory attention networks (Michalka et al., 2015; Noyce et al., 2017; Tobyne et al., 2017). Due to a large degree of inter-subject anatomical variability, identification of these regions depends critically on within-subject analyses. Here, we demonstrate that, for both sexes, an individual's unique pattern of resting-state functional connectivity can accurately identify their specific pattern of visual- and auditory-selective working memory and attention task activation in lateral frontal cortex (LFC) using "connectome fingerprinting." Building on prior techniques (Saygin et al., 2011; Osher et al., 2016; Tavor et al., 2016; Smittenaar et al., 2017; Wang et al., 2017; Parker Jones et al., 2017), we demonstrate here that connectome fingerprint predictions are far more accurate than group-average predictions and match the accuracy of within-subject task-based functional localization, while requiring less data. These findings are robust across brain parcellations and are improved with penalized regression methods. Because resting-state data can be easily and rapidly collected, these results have broad implications for both clinical and research investigations of frontal lobe function. Our findings also provide a set of recommendations for future research.