Biology subjects

Meyyappan, S.

Publications and source records attributed to Meyyappan, S..

11 recordsLinked to original sources

bioRxiv2026

From Attention Control to Stimulus Selection: Neural Mechanisms Revealed by Multivariate Pattern and Functional Connectivity Analyses

Visual spatial attention deployed in advance of sensory stimulation enhances the processing of the stimuli at an attended location. While it is understood that the attention control signals are established even before the stimulus occurs, how these signals help achieve stimulus selection is still not clear. Here, we investigated the neural mechanisms of spatial attention control and subsequent stimulus selection by recording fMRI data from participants performing a cued visual spatial attention task. At the beginning of each trial, participants were cued to covertly attend either the left or the right visual field. Following a random cue-target period, a target stimulus appeared either at the attended location or at the unattended location. Participants discriminated the stimulus appearing at the attended location and ignored the stimulus appearing at the unattended location. Using MVPA decoding and multivariate functional connectivity techniques, we investigated the nature of the information in visual cortex during both the cue and target periods, and further probed how cue-related information was related to target-related sensory processing. The following results were found: (1) attend-left vs. attend-right conditions could be decoded from the cue-period neural activity in visual cortex, (2) target position (target-left vs. target-right) could also be decoded from the target-evoked activity in visual cortex, (3) classifiers built on cue-period neural activity could cross-decode attended target-evoked neural activity in visual cortex and vice versa, (4) higher pattern similarity across cue and target periods, as indexed by cross-decoding accuracy, was correlated with better behavioral performance, (5) the strength of cue-evoked multivariate functional connectivity patterns in visual cortex was positively correlated with behavioral performance, and (6) cue-evoked multivariate functional connectivity patterns were similar to those evoked by the attended targets, and higher connectivity pattern similarity across cue and target periods was correlated with better behavioral performance. These results suggest that top-down attention control enables the formation of (1) a spatial attention template at the level of individual visual cortical areas and (2) an attention network template across visual areas, and these neural patterns support stimulus selection likely via a template matching mechanism at both area and pathway levels.

neuroscience↗

bioRxiv2025

Neural Mechanisms of Willed Attention Control

Cueing paradigms are commonly used to study the neural mechanisms of visual spatial attention control. In these paradigms, each trial starts with an external cue, which instructs the subject to pay covert attention to a spatial location in anticipation of an impending stimulus (instructed attention). Recent work has introduced a new type of cue which prompts the subject to spontaneously decide which spatial location to attend (willed attention). We studied the neural mechanisms of willed attention control by analyzing fMRI and EEG data recorded at two institutions (UF and UC Davis) using the same willed attention paradigm. The findings include: (1) both instructional cues and the choice cue activated the DAN, (2) the choice cue additionally activated a frontoparietal decision network consisting of dorsal anterior cingulate cortex (dACC), anterior insula (AI), anterior prefrontal cortex (APFC), dorsal lateral prefrontal cortex (DLPFC), and inferior parietal lobule (IPL), (3) the decision about where to attend can be decoded in frontoparietal decision network in choice trials but not in instructional trials, and (4) EEG alpha oscillation patterns immediately preceding the choice cue, but not the instructional cues, predicted the postcue direction of attention and the frontoparietal decision network activity. Based on these findings we proposed a model of willed attention control suggesting how the direction of visual spatial attention was decided upon in the absence of external instructions.

neuroscience↗

bioRxiv2025

The Ventral Attention Network Mediates Attentional Reorienting to Cross-Modal Expectancy Violations: Evidence from EEG and fMRI

Our daily interactions with the world are shaped by sensory expectations informed by context and prior experiences, which in turn influence how we allocate our attention. Prominent predictive coding models suggest that sensory expectancy and attention interact but disagree on the precise mechanisms. One possibility is that the Ventral Attention Network (VAN), may play a role by facilitating attentional reorienting when expectancy is violated. To test this, we employed an auditory-visual trial-by-trial cueing paradigm in three experiments integrating EEG and fMRI to investigate the VANs role in violations of cross-modal expectancy. Behavioral results showed faster responses to expected targets, confirming the efficacy of cue-induced expectations in orienting attention to the expected target modality. EEG analyses revealed differences in early ([~]100 ms latency) event-related potentials (ERPs) to both auditory and visual stimuli when expectations were violated. Unexpected stimuli elicited significantly larger early-latency negative ERPs, across both modalities. Source localization of these ERPs and subsequent fMRI evidence revealed activation in the right VAN. Functional connectivity analyses further showed greater coupling between VAN regions and sensory cortices, with modality-specific pathways involving superior temporal gyrus (STG) for auditory and fusiform gyrus (FG) for visual targets. These findings demonstrate that expectancy violations recruit the VAN to reorient attention and resolve sensory conflict. By coordinating top-down control and bottom-up sensory input, the VAN supports adaptive responses to unexpected stimuli. This work advances our understanding of predictive processing in multisensory perception and highlights the VANs central role in flexible cognitive control.

neuroscience↗

bioRxiv2025

Neural mechanisms of object prioritization in vision

Selective attention is widely thought to be sensitive to visual objects. This is commonly demonstrated in cueing studies, which show that when attention is deployed to a known target location that happens to fall on a visual object, responses to targets that unexpectedly appear at other locations on that object are faster and more accurate, as if the object in its entirety has been visually prioritized. However, this notion has recently been challenged by results suggesting that putative object-based effects may reflect the influence of hemifield anisotropies in attentional deployment, or of unacknowledged influences of perceptual complexity and visual clutter. Studies employing measures of behaviour provide limited opportunity to address these challenges. Here, we used EEG to directly measure the influence of task-irrelevant objects on the deployment of visual attention. We had participants complete a simple visual cueing task involving identification of a target that appeared at either a cued location or elsewhere. Throughout each experimental trial, displays contained task-irrelevant rectangle stimuli that could be oriented horizontally or vertically. We derived two cue-elicited indices of attentional deployment-lateralized alpha oscillations and the ADAN component of the event-related potential-and found that these were sensitive to the otherwise irrelevant orientation of the rectangles. Our results demonstrate that the allocation of visual attention is influenced by objects boundaries, supporting models of object-based attentional prioritization.

neuroscience↗

bioRxiv2024

Hierarchical Organization of Visual Feature Attention Control

Attention can be deployed in anticipation of visual stimuli based on features such as their color or direction of motion. This anticipatory feature-based attention involves top-down neural control signals from the frontoparietal network that bias visual cortex to enhance the processing of attended information and suppress distraction. So, for example, anticipatory attention control can enable effective selection based on stimulus color while ignoring distracting information about stimulus motion. But as well, anticipatory attention can be focused more narrowly, for example, to select specific colors or motion directions that define task-relevant events and objects. One important question that remains open is whether anticipatory attention control first biases broad feature dimensions such as color versus motion before biasing the specific feature attributes (e.g., blue vs. green). To investigate this, we recorded EEG activity during a task where participants were cued to either attend to a color (blue or green) or a motion direction (up or down) on a trial-by-trial basis. Applying multivariate decoding approaches to the EEG alpha band (8-12 Hz) activity during the attention control period (cue-target interval), we observed significant decoding for both the attended dimensions (color vs. motion) and specific feature attributes (blue vs. green; up vs. down). Importantly, the temporal onset of the dimension-level biasing (color vs. motion) preceded that of the attribute-level biasing (e.g., blue vs. green). These findings demonstrate that the top-down control of feature-based attention proceeds in a hierarchical fashion, first biasing the broad feature dimension, and then narrowing to the specific feature attribute. Significance StatementDuring voluntary feature-based attention, electrophysiological and neuroimaging studies have highlighted the role of anticipatory (top-down) biasing of the sensory cortex in enhancing the selection of attended stimulus attributes, but little is known about how this is achieved. In particular, it is not clear whether attending to an attribute such as a color (blue vs. green) or motion direction (up vs. down) first biases all neural structures coding that dimension (color/motion) before biasing the specific attribute, or if the top-down signals directly bias only the attended attribute. Using EEG and multivariate decoding, we report that top-down attention control follows a hierarchical organization: first, the broader attended feature dimension is biased, which is followed by the biasing of the specific feature attribute.

neuroscience↗

bioRxiv2023

Top-Down Biasing of Visual Cortical Activity Encodes Attended Information and Facilitates Behavioral Performance in Visual Spatial Attention

In models of visual spatial attention control, it is commonly held that top-down control signals originate in the dorsal attention network, propagating to the visual cortex to modulate baseline neural activity and bias sensory processing. However, the precise distribution of these top-down influences across different levels of the visual hierarchy is debated. In addition, it is unclear whether these changes in baseline neural activity directly translate into improved performance. We analyzed attention-related baseline activity during the anticipatory period of a trial-by-trial voluntary spatial attention task, using two independent fMRI datasets, and two different analytic approaches. First, as in prior studies, univariate analysis showed that covert attention significantly enhanced baseline neural activity in higher-order visual areas contralateral to the attended visual hemifield, while effects in lower-order visual areas (e.g., V1) were weaker and more variable. Second, in contrast, multivariate pattern analysis (MVPA) revealed significant decoding of attention conditions across all visual cortical areas, with lower-order visual areas exhibiting higher decoding accuracies than higher-order areas. Third, decoding accuracy, rather than the magnitude of univariate activation, was a better predictor of a subjects stimulus discrimination performance. Finally, the MVPA results were replicated across two experimental conditions, where the direction of spatial attention was either externally instructed by a cue or based on the participants free choice decision about where to attend. Together, these findings offer new insights into the extent of attentional biases in the visual hierarchy under top-down control, and how these biases influence both sensory processing and behavioral performance. HighlightsO_LIMultivariate pattern analysis revealed the presence of top-down attentional biasing signals in all areas of the visual hierarchy whereas univariate analysis was not able to reveal the full extent of attentional biasing in the visual cortex. C_LIO_LIThe decoding accuracy derived from the MVPA analysis but not the magnitude difference derived from the univariate analysis predicted the subjects behavioral performance in stimulus discrimination. C_LIO_LIThe MVPA results were consistent across two experimental conditions where the direction of spatial attention was driven either by external instructions or from purely internal decisions. C_LI

neuroscience↗

bioRxiv2023

Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks

Multivoxel pattern analysis (MVPA) examines the differences in fMRI activation patterns associated with different cognitive conditions and provides information not possible with the conventional univariate analysis. Support vector machines (SVMs) are the predominant machine learning method in MVPA. SVMs are intuitive and easy to apply. The limitation is that it is a linear method and mainly suitable for analyzing data that are linearly separable. Convolutional neural networks (CNNs), a class of AI models originally developed for object recognition, are known to have the ability to approximate nonlinear relationships. CNNs are rapidly becoming an alternative to SVMs. The purpose of this study is to compare the two methods when they are applied to the same datasets. Two datasets were considered: (1) fMRI data collected from participants during a cued visual spatial attention task (the attention dataset) and (2) fMRI data collected from participants viewing natural images containing varying degrees of affective content (the emotion dataset). We found that (1) both SVM and CNN are able to achieve above chance level decoding accuracies for attention control and emotion processing in both the primary visual cortex and the whole brain with, (2) the CNN decoding accuracies are consistently higher than that of the SVM, (3) the SVM and CNN decoding accuracies are generally not correlated with each other, and (4) the heatmaps derived from SVM and CNN are not significantly overlapping. These results suggest that (1) there are both linearly separable features and nonlinearly separable features in fMRI data that distinguish cognitive conditions and (2) applying both SVM and CNN to the same data may yield a more comprehensive understanding of neuroimaging data. Key pointsO_LIWe compared the performance and characteristics of SVM and CNN, two major methods in MVPA analysis of neuroimaging data, by applying them to the same two fMRI datasets. C_LIO_LIBoth SVM and CNN achieved decoding accuracies above chance level for both datasets in the chosen ROIs and the CNN decoding accuracies were consistently higher than those of SVM. C_LIO_LIThe heatmaps derived from SVM and CNN, which assess the contribution of voxels or brain regions to MVPA decoding performance, showed no significant overlap, providing evidence that the two methods depend on distinct brain activity patterns for decoding cognitive conditions. C_LI

bioinformatics↗

bioRxiv2022

Imaging the Neural Substrate of Trigeminal Neuralgia Pain Using Deep Learning

Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were six regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex(dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions represent signature centers of TN pain and provide target areas for future studies relating to central mechanisms of TN.

neuroscience↗

bioRxiv2022

Top-down control of the left visual field bias in cued visual spatial attention

A left visual field (LVF) bias in perceptual judgements, response speed and discrimination accuracy are well documented in humans. However, LVF bias can be modulated by perceptual and task demands. For example, cuing spatial attention can reduce or eliminate the LVF bias, suggesting that attentional control can compensate for the LVF bias. We investigated this possibility directly by recording pupillometry together with fMRI in a cued visual spatial attention task. Prior to the onset of a task-relevant target stimulus, we observed that the pupil was significantly more dilated following attend-right than attend-left cues even though task performance did not differ. This difference in pupil dilation was inversely related to the corresponding difference in later target-evoked pupil dilation and in the reaction times to those targets, suggesting that an increased attentional effort was triggered by the attend-right cues, and this offset the LVF bias, equating behavioral performance. The differences in pupil dilation to the right versus left hemifield were correlated with corresponding fMRI differences primarily in the right hemisphere, supporting the idea that the increased attentional effort for rightward attention is mediated by activity in right hemisphere networks, which illuminates how attentional control mediates attentional biases in vision.

neuroscience↗

bioRxiv2021

Effects of Frontoparietal Theta tACS on Verbal Working Memory: Behavioral and Neurophysiological Analysis

Left-lateralized frontoparietal theta oscillations are thought to play an important role in verbal working memory. We causally tested this idea by stimulating the frontoparietal theta network at individual theta frequencies (4 to 8 Hz) during verbal working memory and observing the subsequent behavioral and neurophysiological effects. Weak electric currents were delivered via two 4x1 HD electrode arrays centered at F3 and P3. Three stimulation configurations, including in-phase, anti-phase, or sham, were tested on three different days in a cross-over design. On each test day, the subject underwent three experimental sessions: pre-, during- and post-stimulation sessions. In all sessions, the subject performed a Sternberg verbal working memory task with three levels of memory load (load 2, 4 and 6), imposing three levels of cognitive demand. Analyzing behavioral, EEG, and pupillometry data from the post-stimulation sessions, we report three results. First, in-phase stimulation improved task performance only in subjects with higher working memory capacity (WMC) and under higher memory load (load 6). Second, in-phase stimulation enhanced frontoparietal theta synchrony during working memory retention only in subjects with higher WMC under higher memory loads (load 4 and load 6), and the enhanced frontoparietal theta synchronization is mainly driven by enhanced frontal[->]parietal theta Granger causality. Third, the pupil diameter was not different irrespective of whether the preceding stimulation was in-phase, anti-phase, or sham. These findings suggest that theta tACS effects on verbal working memory were load- and subject-dependent, rooted in tACS-induced changes in frontoparietal network interactions, and not driven by changes in arousal levels.

neuroscience↗

bioRxiv2020

Role of Inferior Frontal Junction (IFJ) in the Control of Feature vs Spatial Attention

Feature-based attention refers to preferential selection and processing of items and objects based on their non-spatial attributes such as color or shape. While it is intuitively an easier form of attention to relate to in our day to day lives, the neural mechanisms of feature-based attention are not well understood. Studies have long implicated the dorsal attention network as a key control system for voluntary spatial, feature and object-based attention. Recent studies have expanded on this model by focusing on the inferior frontal junction (IFJ), a region in the pre-frontal cortex to be the source of feature attention control, but not spatial attention control. However, the extent to which IFJ contributes to spatial attention remains a topic of debate. We investigated the role of IFJ in the control of feature versus spatial attention in a cued visual spatial (attend left or right) and feature attention (attend red or green) task using fMRI. Analyzing single-trial cue-evoked fMRI responses using univariate GLM and multi-voxel pattern analysis (MVPA), we observed the following. First, the univariate BOLD activation responses yielded no significant differences between feature and spatial cues. Second, MVPA analysis showed above chance level decoding in classifying feature attention (attend-red vs. attend-green) in both the left and right IFJ, whereas during spatial attention (attend-left vs. attend-right) decoding was at chance. Third, while the cue-evoked decoding accuracy was significant for both left and right IFJ during feature attention, target stimulus-evoked neural responses were not different. Importantly, only the connectivity patterns from the right IFJ was predictive of target-evoked activity in visual cortex (V4); this was true for both left and right V4. Finally, the strength of this connectivity between right IFJ and V4 (bilaterally) was found to be predictive of behavioral performance. These results support a model where the right IFJ plays a crucial role in top down control of feature but not spatial attention.

neuroscience↗