Biology subjects

Ries, A. J.

Publications and source records attributed to Ries, A. J..

7 recordsLinked to original sources

bioRxiv2025

Mapping neural activity during naturalistic visual and memory search

In everyday life, individuals often search for one of several items stored in memory. This cognitive process, known as hybrid search, is critical for tasks like navigating using landmarks. While the behavioral aspects of hybrid search have been extensively studied, the underlying neural mechanisms remain less understood. In this study, we combined concurrent magnetoencephalography (MEG) and eye movement recordings to investigate the oscillatory and evoked neural dynamics supporting hybrid search in naturalistic settings. Twenty-one participants (12 males, 9 females) performed a free-viewing task involving visual search for targets embedded in memory (hybrid search) across naturalistic scenes. Time-Frequency analyses revealed specific neural signatures during memory encoding, retention, and visual search. During encoding and retention, posterior alpha-band power decreased with memory load, reflecting heightened perceptual and mnemonic demands. During visual search, frontoparietal beta-band activity scaled with memory load, suggesting increased cognitive control. By aligning MEG signals to eye movement events and applying source reconstruction, we identified an early visually evoked lambda response, localized to V1, followed by a distributed P3m component, with maximum activation in the right inferior parietal lobe, that discriminated target from distractor fixations. Together, these findings demonstrate how oscillatory and evoked responses dynamically support hybrid search in naturalistic settings, revealing how memory, attention, and visual processing interact during active vision. Significance statementUnderstanding how the brain supports everyday tasks, like searching for familiar objects, requires studying cognition under realistic conditions, yet most knowledge comes from highly controlled lab tasks with simple stimuli. Here, we combined MEG and eye-tracking to examine how the brain supports free-viewing hybrid search -looking for any of several remembered items-across natural scenes. We found memory load modulations in posterior alpha oscillations during encoding and retention, as well as in frontoparietal beta oscillations during search. By time-locking neural signals to eye movements, we identified a robust marker of target detection. These findings provide direct evidence of how attention, memory, and vision interact in realistic settings, helping bridge the gap between controlled experimental paradigms and real-world cognition.

neuroscience↗

bioRxiv2024

DJ-1 mediates regulation of metabolism and immune response in Parkinsons disease astrocytes and Glioblastoma cells

An inverse correlation for the expression of Parkinsons disease (PD)- and cancer-associated genes has been previously reported. Genes that are upregulated in cancer are frequently downregulated in PD and vice versa. PARK7, encoding DJ-1, was initially identified as an oncogene, but loss of DJ-1 causes early-onset PD. However, it remains elusive how differential DJ-1 levels contribute to opposite cell fates in cancer and PD. Here, we demonstrate specific effects of differential DJ-1 protein levels on the energy metabolism and cell growth in patient-derived cellular models of PD and glioblastoma (GBM) cell lines. Impaired energy metabolism was associated with an increased immune response upon IL-1{beta} stimulation and increased apoptosis and decreased cell growth in models of PD, whereas in GBM cells increased metabolic activity translated into a reduced immune response and increased cell growth. Furthermore, we found decreased glutathione (GSH) synthesis and therefore increased levels of reactive oxygen species (ROS) and oxidized glutathione (GSSG) in models of DJ-1 deficiency and decreased ROS levels in GBM cell lines. Thus, the mechanism by which DJ-1 modulates these phenotypes is the same in both diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/621212v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@4eccb4org.highwire.dtl.DTLVardef@161669corg.highwire.dtl.DTLVardef@197bfc3org.highwire.dtl.DTLVardef@1aecb36_HPS_FORMAT_FIGEXP M_FIG C_FIG DJ-1 levels modulate GSSG/GSH ratio and ROS levels, which results in divergent effects on cell growth and immune response in DJ-1-dependent glial pathologies in glioblastoma and PD. In models of PD, DJ-1 level dependent phenotypes can be rescued by antioxidant treatment that reduces the GSSG/GSH ratio and ROS levels.

neuroscience↗

bioRxiv2024

Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR

Gaze-assisted interaction techniques enable intuitive selections without requiring manual pointing but can result in unintended selections, known as Midas touch. A confirmation trigger eliminates this issue but requires additional physical and conscious user effort. Brain-computer interfaces (BCIs), particularly passive BCIs harnessing anticipatory potentials such as the Stimulus-Preceding Negativity (SPN) - evoked when users anticipate a forthcoming stimulus - present an effortless implicit solution for selection confirmation. Within a VR context, our research uniquely demonstrates that SPN has the potential to decode intent towards the visually focused target. We reinforce the scientific understanding of its mechanism by addressing a confounding factor - we demonstrate that the SPN is driven by the users intent to select the target, not by the stimulus feedback itself. Furthermore, we examine the effect of familiarly placed targets, finding that SPN may be evoked quicker as users acclimatize to target locations; a key insight for everyday BCIs. CCS CONCEPTSO_LIHuman-centered computing [->] Virtual reality; Mixed / augmented reality; Accessibility technologies; Interaction techniques. C_LI ACM Reference FormatG. S. Rajshekar Reddy, Michael J. Proulx, Leanne Hirshfield, and Anthony J. Ries. 2024. Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI 24), May 11-16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 17 pages. https://doi.org/10.1145/3613904.3641925

neuroscience↗

bioRxiv2023

Investigating the effects of contextual information, visual working memory, and inhibitory control in hybrid visual search

In real-life scenarios, individuals frequently engage in tasks that involve searching for one of various items stored in memory. This combined process of visual search and memory search is known as hybrid search. To date, most hybrid search studies have been restricted to average observers looking for previously well-memorized targets in blank backgrounds. Here we investigated the effects of context and the role of memory in hybrid search by modifying the tasks memorization phase to occur in all-new single trials. Additionally, we aimed to assess how individual differences in visual working memory capacity and inhibitory control influence performance during hybrid search. In an online experiment, 110 participants searched for potential targets in images with and without context. A change detection and go/no-go task were also performed to measure working memory capacity and inhibitory control, respectively. We show that, in target present trials, the main hallmarks of hybrid search remain present, with a linear relationship between reaction time and visual set size, and a logarithmic relationship between reaction time and memory set size. Context affected search efficiency in different ways. In target-absent trials we found large differences between context-present and absent conditions, suggesting participants adoption of adaptive strategies. Finally, working memory capacity did not predict most search performance measures. Inhibitory control, when relationships were significant, could account for only a small portion of the variability in the data. This study provides insights into the effects of context and individual differences on search efficiency and termination.

animal behavior and cognition↗

bioRxiv2021

Identification of Target Objects from Gaze Behavior during a Virtual Navigation Task

Eye tracking has been an essential tool within the vision science community for many years. However, the majority of studies involving eye-tracking technology employ a relatively passive approach through the use of static imagery, prescribed motion, or video stimuli. This is in contrast to our everyday interaction with the natural world where we navigate our environment while actively seeking and using task-relevant visual information. For this reason, vision researchers are beginning to use virtual environment platforms, which offer interactive, realistic visual environments while maintaining a substantial level of experimental control. Here, we recorded eye movement behavior while participants freely navigated through a complex virtual environment. Within this environment, participants completed a visual search task where they were asked to find and count occurrence of specific targets among numerous distractor items. We assigned each participant into one of four target groups: Humvees, motorcycles, aircraft, or furniture. Our results show a significant relationship between gaze behavior and target objects across subject groups. Specifically, we see an increased number of fixations and increase dwell time on target relative to distractor objects. In addition, we included a divided attention task to investigate how search patterns changed with the addition of a secondary task. With increased cognitive load, subjects slowed their speed, decreased gaze on objects, and increased the number of objects scanned in the environment. Overall, our results confirm previous findings from more controlled laboratory settings and demonstrate that complex virtual environments can be used for active visual search experimentation, maintaining a high level of precision in the quantification of gaze information and visual attention. This study contributes to our understanding of how individuals search for information in a naturalistic virtual environment. Likewise, our paradigm provides an intriguing look into the heterogeneity of individual behaviors when completing an un-timed visual search task while actively navigating.

neuroscience↗

bioRxiv2021

During natural viewing, neural processing of visual targets continues throughout saccades

Relatively little is known about visual processing during free-viewing visual search in realistic dynamic environments. Free-viewing is characterized by frequent saccades. During saccades, visual processing is thought to be inhibited, yet we know that the pre-saccadic visual content can modulate post-saccadic processing. To better understand these processes in a realistic setting, we study here saccades and neural responses elicited by the appearance of visual targets in a realistic virtual environment. While subjects were being driven through a 3D virtual town they were asked to discriminate between targets that appear on the road. We found that the presence of a target enhances early occipital as well as late frontocentral saccade-related responses. The earlier potential, shortly after 125ms post-saccade onset, was enhanced for targets that appeared in peripheral vision as compared to central vision, suggesting that fast peripheral processing initiated before saccade onset. The later potential, at 195ms post-saccade onset, was strongly modulated by the visibility of the target with a spatial distribution reminiscent of the classic P300 response. Together these results suggest that, during natural viewing, neural processing of the pre-saccadic visual stimulus continues throughout the saccade, apparently unencumbered by saccadic inhibition.

neuroscience↗

bioRxiv2019

Decoding P300 Variability using Convolutional Neural Networks

Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis. In this article, we train our CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment. We analyze the CNN output as a function of the underlying variability in the P300 response and demonstrate that the CNN output is sensitive to the experiment-induced changes in the neural response. We then assess the utility of our approach as a means of improving the overall signal-to-noise ratio in the EEG record. Finally, we show an example of how CNN-based decoding can be applied to the analysis of complex data.

neuroscience↗