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11:00 - 12:30
Parallel sessions 5
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11:00 - 12:30
Room: C-Building - N14
Chair/s:
Kathrin Finke, Ingrid Scharlau, Jan Tünnermann
Part II of the symposium on the Theory of Visual Attention (TVA) extends Part I, moving to research that highlights TVA’s potential for measuring  attentional changes in diverse populations, relating them to underlying neural changes, perceptual and awareness phenomena. Simon Schrenk opens with a machine-learning study linking resting-state functional connectivity to TVA parameters—visual processing speed (C), short-term visual memory capacity (K), and top-down control (α)—in healthy older adults. This work identifies distinct neural network signatures for each attentional component, providing a framework for connecting TVA-based measures with intrinsic brain organization in aging. Hannah Klink et al. follow by demonstrating that alterations within frontoparietal networks are associated with reduced top-down control in patients with mild cognitive impairment, situating TVA within altered brain-network dynamics. Thomas Sørensen presents findings on expectancy modulations interacting with the κ parameter, offering new perspectives on attentional weighting within the TVA framework. 
Solveig Menrad’s talk relates attentional parameters in patients with ADHD to subjective and objective polysomnographic measures of sleep quality in patients with ADHD. Finally, Kathrin Finke, Jan Tünnermann and Ingrid Scharlau will discuss the development and challenges of TVA. Together, these contributions aim to chart the clinical frontiers of TVA—linking theory, neural markers, and potential translational uses in diverse populations. 
Submission 422
Machine Learning on Resting-State Connectivity Reveals Neural Predictors of TVA Parameters (Visual Processing Speed, Visual STM Capacity, Top-down Control) in Aging Brains
SymposiumTalk-01
Presented by: Simon Schrenk
Simon Schrenk 1, Adriana L. Ruiz-Rizzo 1, Otto W. Witte 1, Stefan Brodoehl 1, Kathrin Finke 1, 2, 3
1 Department of Neurology, Jena University Hospital, Germany
2 Center of Sepsis Control and Care, Jena University Hospital, Germany
3 Department of Psychology, University of Munich, Germany
Introduction: Research on age-related changes in visual attention has increasingly focused on neural underpinnings, yet evidence linking individual differences in attentional functions in older adults to those in resting-state functional connectivity (rsFC) remains limited. This study used a predictive modeling approach exploring whether machine learning can reveal associations between rsFC and parameters derived from the Theory of Visual Attention (TVA) in healthy older adults.

Methods: 87 healthy older adults (mean age = 66.03 years, 62 female) underwent resting-state functional magnetic resonance imaging and TVA-based assessment of three key visual attention parameters: visual processing speed (VPS) C, visual short-term memory capacity (vSTM) K, and top-down control α. We identified the most predictive intra- and internetwork rsFC features for individual differences in each of these parameters using a machine-learning model.

Results: The model classified participants’ performance in the TVA parameters with 84 to 85% accuracy. Top-down control α was best predicted by connectivity between the visual and frontoparietal (FP) networks, and between the motor (MOT) and ventral attention networks, VPS C by connectivity between FP and MOT, and vSTM K by connectivity between the default mode network with the FP and dorsal attention network.

Discussion: These results reveal distinct rsFC network associations for VPS, vSTM, and top-down control, indicating that these functions rely on separate neural processes in healthy older adults and suggesting that the respective networks are crucial for maintaining cognitive functions that may decline with age. As a next step, we will test if these results are replicable.