# Predictive Processing Over the Course of Aging: Multiple Timescales of Effective Connectivity

**Authors:** Martin Tom Banaschewski, Christoph Mathys, István Winkler, Juanita Todd, Ryszard Auksztulewicz

PMC · DOI: 10.1111/ejn.70387 · 2026-01-07

## TL;DR

This study explores how aging affects the brain's ability to adapt to changing sounds over time, using EEG and modeling to track neural connections.

## Contribution

The paper reveals age-related changes in multi-timescale neural adaptation during predictive auditory processing for the first time.

## Key findings

- ERPs showed stronger responses to original deviants than reverse deviants, with amplitudes increasing on short timescales but declining over longer timescales and with age.
- Dynamic causal modeling revealed increased descending connectivity for original deviants and timescale-dependent intrinsic connectivity changes.
- Aging was linked to stronger descending connectivity modulation by deviant type but weaker modulation by slow dynamics.

## Abstract

Predictive processing theories describe perception as a dynamic interplay between top‐down predictions and bottom‐up prediction errors across hierarchical stages of sensory processing. However, it remains unclear how neural connectivity flexibly adapts to changing sensory environments over time, and how these dynamics are influenced by aging. This study investigated how temporal factors on three distinct timescales, as well as age, shape neural responses and connectivity to dynamically changing auditory stimuli. Electroencephalography (EEG) data were recorded from 63 participants aged 18–75 as they listened to sequences of tones, where rare and unexpected “original deviants” became standards over time, and previously standard tones became “reverse deviants.” Event‐related potentials (ERPs) were more pronounced for original deviants than reverse deviants. Amplitudes increased on short timescales (seconds) but declined over longer timescales (minutes) and with advancing age. To infer the neural mechanisms underlying these effects, dynamic causal modelling (DCM) was used to analyze effective connectivity. DCM revealed increased descending (top‐down) connectivity for original deviants, consistent with a stronger reliance on predictions. Additionally, intrinsic (within‐region) connectivity increased over seconds but decreased over minutes, reflecting timescale‐dependent neural adaptation. Aging was associated with stronger modulation of descending connectivity by deviant type but weaker modulation by slow dynamics. These results underscore the brain's ability to dynamically adapt to changing sensory environments at multiple timescales and for the first time reveal age‐related changes in the dynamics of this adaptation.

This study used electroencephalography (EEG) and dynamic causal modelling to examine how predictive auditory processing and neural connectivity adapt over time and with age. Sixty‐three participants (ages 18–75) listened to tone sequences where deviant and standard roles reversed between contexts, probing prediction at multiple timescales (seconds to minutes). The design allowed analysis of ERPs and connectivity changes linked to context effects, multi‐timescale dynamics, and aging.

## Full-text entities

- **Diseases:** MoCA (MESH:D003072), deficits in basic auditory processing (MESH:D001308), memory deficits (MESH:D008569), head injuries (MESH:D006259), PP (MESH:D010335), DCM (MESH:D004195), MMN (MESH:C536928), neurological or psychiatric disorders (MESH:D001523), schizophrenia (MESH:D012559), age-related hearing loss (MESH:D010024), hearing loss (MESH:D034381)
- **Chemicals:** BMA (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12779497/full.md

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Source: https://tomesphere.com/paper/PMC12779497