Summary: For the first time, a multinational research team has mapped how the brain’s electrical activity evolves from age 5 to 100 by linking it directly to the brain’s physical “wiring diagram.” The study introduces Xi–αNET, a generative model that explains how nerve-signal speed and anatomical connections create the patterns seen on an EEG.
By analyzing the HarMNqEEG dataset—recordings from 1,965 people across nine countries—researchers discovered that the slowing of brain waves in old age isn’t random; it is a direct reflection of declining myelin (the insulation on nerve fibers). This breakthrough suggests that simple EEG tests could become a “speedometer” for brain health, flagging neurodegenerative diseases like Parkinson’s before traditional symptoms appear.
Key Facts
- The Xi–αNET Model: This new framework treats the brain’s “background noise” ($\xi$) and rhythmic alpha waves ($\alpha$) as independent processes driven by physical signal-conduction speeds.
- The U-Shaped Journey: Nerve-signal delays follow a U-shaped curve over a lifetime—they are short in youth, stable in midlife, and lengthen significantly in old age as white matter integrity declines.
- Myelin as the “Pace Setter”: The study proved that the frequency of alpha waves is set by the thickness of myelin insulation. Heavier myelination equals faster conduction and higher-frequency brain waves.
- Clinical “Red Flags”: The model successfully detected the signature “slowing” of alpha rhythms in patients with Parkinson’s disease, proving its potential as a diagnostic tool.
Source: Science China Press
How does the human brain’s electrical activity grow from childhood, peak in adulthood, and decline in older age?
A multinational team has tackled this question by linking the brain’s “wiring diagram” and signal‑conduction speed to two familiar features of an electroencephalogram (EEG): the broadband background activity (ξ, pronounced “xi”) and the more rhythmic alpha waves.
Their work, published in National Science Review, introduces a new model called Xi–αNET (“Xi–AlphaNET”) that explains how anatomical connections and nerve‑signal delays give rise to these patterns and how they change over the lifespan.
At the heart of the study is the HarMNqEEG dataset, a unique collection of resting‑state EEG recordings from 1,965 people aged five to 100 years. Participants were scanned in nine countries using 12 different EEG systems, and the data were harmonized to allow meaningful comparisons. Such breadth allowed the researchers to probe how the brain’s rhythms develop across an entire century of life.
Traditional analyses treat alpha waves and the background ξ signal as statistical patterns divorced from brain structure. Xi–αNET instead treats the aperiodic background (ξ) and the α‑rhythm as independent processes generated by the brain’s network.
The model uses a myelination map derived from MRI to create a hierarchy of brain regions, then estimates how signals flow through this hierarchy. It shows that across the lifespan the broadband activity is localized in frontal regions and dominated by feedforward connections (from sensory areas upward), while the α‑rhythm is strongest in posterior sensory and sensorimotor regions and dominated by feedback connections (top‑down influences).
This distinction echoes previous theories linking slower rhythms to long‑range feedback and faster rhythms to feedforward processing.
Xi–αNET also incorporates information about how long it takes for activity in one cortical region to reach another. These conduction delays are not measured directly by EEG; rather, they come from intracranial cortico‑cortical evoked responses, which provide priors on the time it takes for signals to travel between regions.
The model then estimates a subject‑specific overall delay to align these prior delays to each individual. When the team examined how these delays vary with age, they found a U‑shaped trajectory—shorter delays in youth, stable midlife values, and longer delays in older age.
Comparing this trajectory with independent MRI‑derived maps of myelination revealed that the curves closely match. In other words, the degree of insulation around nerve fibers (myelin) appears to set the pace of brain rhythms: faster conduction, reflecting heavier myelination, corresponds to higher alpha frequencies.
The strong inverse relationship—peak alpha frequency declines as conduction delays lengthen—suggests that slowing alpha waves may be an accessible marker of declining white‑matter integrity in aging or disease.
Beyond its scientific insights, the work demonstrates the power of generative models—mathematical frameworks that explicitly link structure to function. The authors show that Xi–αNET produces reliable estimates of cortical activity, effective connectivity and subject‑specific conduction delays from routine EEG recordings.
Such tools could pave the way for normative reference charts, against which individual deviations might flag developmental disorders, neurodegenerative diseases, or the effects of interventions. Preliminary analyses in the paper show that the model can detect the slowing of alpha rhythms in Parkinson’s disease, hinting at future clinical applications.
This study paints a new picture of brain rhythms: they are not free‑floating oscillations but reflections of the brain’s physical wiring and the efficiency of its signal highways. As lead author Ronaldo Garcia Reyes puts it, “By weaving together structural connections, conduction speed and electrical rhythms, we can start to understand how the brain’s architecture shapes its dynamics and why these dynamics change with age.”
Key Questions Answered:
A: It’s a matter of “insulation.” Your nerves are wrapped in myelin, which acts like the rubber coating on an electrical wire. As we age, this insulation thins out. The Xi–αNET model shows that when this “coating” degrades, the signals take longer to travel, which physically forces your brain’s alpha waves to slow down.
A: Potentially, yes! Because the study mapped the “normal” signal speeds for every age from 5 to 100, doctors can now compare your EEG against a global “normative chart.” If your signal delays are much longer than average for your age, it could be an early warning sign of a condition like Parkinson’s or dementia.
A: Think of the background noise as the baseline hum of the brain’s sensory “uploading” (feedforward) process, mostly active in the front of the head. Alpha waves are the rhythmic “feedback” signals (top-down) that help us focus and process sensory info, mostly active in the back of the head.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this AI and neuroscience research news
Author: Bei Yan
Source: Science China Press
Contact: Bei Yan – Science China Press
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Lifespan Development of EEG Alpha and Aperiodic Component Sources is Shaped by the Connectome and Axonal Delays” by Ronaldo Garcia Reyes, Ariosky Areces Gonzalez, Ying Wang, Yu Jin, Shahwar Yasir, Maria Luisa Bringas-Vega, Mitchell Valdes-Sosa, Cheng Luo, Peng Xu, Viktor Jirsa, Dezhong Yao, Ludovico Minati, and Pedro A. Valdes-Sosa. National Science Review
DOI:10.1093/nsr/nwag076
Abstract
Lifespan Development of EEG Alpha and Aperiodic Component Sources is Shaped by the Connectome and Axonal Delays
We introduce ξ-αNET, a model of cortical activity that represents the EEG aperiodic (ξ) and α-rhythm (α) components as Hida-Matérn processes constrained by anatomical connectivity and interareal conduction delays.
This approach integrates the decomposition of Spectral Granger Causality and quantifies the lifespan trajectories of spectral processes. Using Bayesian inversion on cross-spectral rsEEG data from 1,965 participants aged 5-100 years (HarMNqEEG dataset), the model estimates cortical activity showing high test-retest reliability, effective connectivity patterns, and conduction delays.
Given the approximate cortical hierarchy inferred from the inverted T1w/T2w myelination map, used as a proxy for feedforward and feedback organization, the aperiodic and α components reveal opposite directional networks across the lifespan, where the aperiodic component is localized in the frontal cortex and the α component is localized in the posterior cortex, with feedforward and feedback directed connections, respectively.
For both processes, we found that the spectral parameters follow a nonlinear inverted U-shape lifespan trajectory. Finally, the model uniquely estimates global conduction delays, which were negatively correlated with α frequency and with independent cortical myelination (T1w/T2w) measures, consistent with a mechanistic link between conduction delays and α-rhythm modulation.
