Friday, Jun 27: 11:30 AM - 12:45 PM
Oral Sessions
Brisbane Convention & Exhibition Centre
Room: P2 (Plaza Level)
Presentations
In task-based fMRI, collinearity between design matrix regressors can impact power. Optimal task design involves evaluating multiple designs to maximize efficiency and minimize collinearity. Here, we highlight inappropriate strategies to reduce collinearity that introduce biases, impair contrast interpretability, and potentially increase false positives. Using Monetary Incentive Delay (MID) task fMRI data from the Adolescent Brain Cognitive Development (ABCD) study, we show that omitting regressors, using impulse regressors for extended activations, and ignoring response times bias contrast estimates, sometimes inflating error rates in a sample of 500 subjects. We propose a "Saturated" model that includes all stimuli and response times, eliminating bias and providing valid estimates of task-related brain activity.
Fear conditioning, also known as threat conditioning, is a psychological paradigm developed over a century ago, which models how the association between an initially neutral stimulus (conditioned stimulus, CS) and an innately aversive stimulus (unconditioned stimulus, US) is learned. It remains one of the most widely used and productive experimental models for investigating both normal and pathological fear and anxiety in humans (Beckers,
et al., 2023). Differences between the findings of prior studies, may be explained by individual differences, such as sociodemographic factors (e.g., age) and trait variables (e.g., trait anxiety), or task-specific variables, such as task instructions or the choice of aversive stimuli, that differ between studies, and are likely to modulate the neural correlates of fear conditioning (Lonsdorf et al., 2017).
Presenter
Hannah Savage, Institute of Cognitive Neuroscience
Psychology and Language Studies
London, NA
United Kingdom
Research Background:
Functional Magnetic Resonance Imaging (fMRI) is a pivotal technique in understanding brain responses associated with cognitive functions or during resting states. Traditional studies often assume that a set of homogeneous cognitive functions is attached to specific task paradigms, expecting similar responses across all participants. However, this fundamental hypothesis remains largely untested due to the lack of personalized cognitive processing data, such as personalized brain activation templates.
Brain Measurements in fMRI:
Brain activations and functional connectivity (FC) are the two primary measurements in fMRI studies. While brain activation reflects cognition-specific responses, functional connectivity represents the general network of brain interactions. Previous studies have rarely analyzed these two measurements together.
Motivation and Objective:
Inspired by the similarity between the diffusion model's generation process and the signal transmission and generation in the brain, this study aims to address whether combining brain activation and functional connectivity information can enhance our understanding of brain responses to different task conditions.
Presenter
Rongquan Zhai, Fudan University
Fudan University
Shanghai, Shanghai
China
"Naturalistic imaging" paradigms, where participants watch movies during fMRI, have gained popularity over the past two decades. Many movie-watching studies measure inter-subject correlation (ISC), which refers to the correlation between participants' neural activation time series. Previous research has focused on explaining ISC differences during movie-watching based on individual states and traits, such as social distance, personality, and political orientation. For example, friends show higher ISC than strangers while watching movies. However, movies are not natural categories but cultural artifacts that evoke varying levels of ISC depending on content, directing style, or editing methods. This raises questions about how much trait- or state-like differences in ISC depend on the specific movies chosen, potentially limiting the generalizability of findings across different movies.
Functional magnetic resonance imaging (fMRI) tasks often produce signals that are difficult to detect, especially when studying individual differences (Poldrack et al., 2017, Elliott et al., 2020). Researchers often prioritize power in their analytic workflows by reducing collinearity (Liu et al., 2001). The Monetary Incentive Delay (MID; Knutson et al., [2001]) task has a multi-component trial structure that can exacerbate biases in the estimated BOLD activity when behavioral and BOLD timeseries are misaligned and subject-level models omit task-relevant regressors. Here, we highlight a GE timing issue and important model misspecification in MID subject-level models, and its impact on the estimated BOLD activity released as part of the Adolescent Brain Cognitive Development (ABCD) study® fMRI data.
The ability of the brain to anticipate incoming sensory information is crucial for navigating our dynamic environments, in which we are often confronted with incomplete and noisy inputs. In auditory environments, actively predicting what we will hear next facilitates auditory stream segregation and understanding speech in noise [6,8,9], illustrating the relevance of predictive processing in auditory perception. In predictive coding (PC), it is postulated that the brain actively infers underlying probable causes of sensory input. This process of inference occurs through hierarchical exchange of information across brain areas [2,5]. Within this framework, feedforward and feedback streams perform specialized roles, in which predictions are fed back and prediction-errors (the mismatch between predictions and sensory input) are fed forward to higher-order areas [4].
Here, we probe violations of expectations by presenting sound sequences that are either predictable, deviate from predictions or omit part of the sequence while measuring laminar gradient echo blood oxygenation level-dependent (GE-BOLD) responses using high-resolution functional magnetic resonance imaging (fMRI).
Presenter
Lonike Faes, Maastricht University Maastricht, Limburg
Netherlands