LIP External Colloquium: Satoshi Usami, The University of Tokyo, Japan - "Longitudinal Data Analysis for Inferring Within-Person Relations: Recent Methodological Issues in Model Selection and Estimation"

LIP External Colloquium: Satoshi Usami, The University of Tokyo, Japan - will present:

"Longitudinal Data Analysis for Inferring Within-Person Relations: Recent Methodological Issues in Model Selection and Estimation"

Abstract:
In longitudinal research in psychology and related fields, the inference of within-person relations, that is, relations among changes occurring within individuals, is one of the central themes. In particular, the random intercept cross-lagged panel model (RI-CLPM), which is intended to infer reciprocal relations within individuals, has rapidly become widely used over the past decade. At the same time, debates concerning the appropriateness of using the RI-CLPM and the selection of related models are still ongoing.

In this presentation, focusing especially on longitudinal studies based on a small number of measurement occasions, I will explain the RI-CLPM and other candidate models and organize the key issues surrounding model selection. I will also briefly introduce recent methodological developments rooted in extensions of structural equation modeling (SEM), which is the methodological foundation of the RI-CLPM. For example, I will provide an overview of matrix decomposition-based estimation approaches and their applications for addressing the problems of improper solutions that frequently arise when the RI-CLPM, or models that extend it by incorporating measurement error, are estimated by maximum likelihood. I will also provide an overview of robust estimation and its significance when inferring within-person reciprocal relations as relations among latent variables, as in the RI-CLPM.

In this talk, I will discuss the use of real-time fMRI neurofeedback training in cognitive aging, focusing on its potential both as an intervention and as a tool for investigating the neural mechanisms that support cognition in later life. Real-time fMRI neurofeedback provides individuals with moment-to-moment information about activity in specific brain regions or brain networks, allowing them to learn to voluntarily modulate that activity and enabling researchers to test causal links between brain function and behavior. Selective attention—our ability to focus amid distractions—often declines with age, partly due to reduced functioning in the dorsal anterior cingulate cortex (dACC). Real-time fMRI neurofeedback training allows individuals to receive information about and learn to adjust their own brain activity, but its use and benefits among older adults are not well understood. In the study that I present in this talk, younger and older adults were trained to increase or decrease dACC activity while performing a demanding selective attention task. Older adults who were trained with real-time fMRI neurofeedback to increase dACC activity showed clear gains: higher reward scores, increased dACC activation, and improved task performance through faster and more accurate responses. These effects did not appear in younger adults or those older adults who were trained to decrease dACC activity. Findings highlight preserved brain plasticity in aging and suggest real-time neurofeedback as a viable training modality in cognitive aging. I will discuss future applications to other domains central to cognitive aging, including decision making and spatial navigation, illustrating how this neuroimaging approach may promote cognitive resilience while advancing causal mechanistic research on brain–behavior relationships across the adult lifespan.

Please use this link for joining hybrid:

https://mpib-berlin.webex.com/mpib-berlin/j.php?MTID=med667fb14fa2a1f8e5f98f6189bb32f9

Meeting number: 2741 607 6544

Password: 8bKvH8EbCe6