Anatomically accurate digital twin of 2-year-old's brain uncovers neural signatures linked to autism

by Sanjukta Mondal, Medical Xpress

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edited by Sadie Harley, reviewed by Robert Egan

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For decades, researchers have been trying to understand the biological roots of autism spectrum disorder (ASD), a common neurodevelopmental condition that shapes how people communicate, learn and interact with the world. One of the major hurdles is that the brain's neural networks are extraordinarily complex. Existing models still lack the detail needed to capture both the brain's structure and its dynamic activity in a unified manner.

In a recent study published in PLOS Digital Health, researchers created a new system called FEDE (high FidElity Digital brain modEl) that builds a digital twin, a detailed computer replica or virtual copy of a real-world object. In this study, it was a virtual copy of the brain of a 2-year-old child with ASD.

To build FEDE, researchers combined maps of the child's brain structure obtained using MRI with mathematical modeling to create a digital brain that can simulate both how the brain is built and how it functions.

The digital twin replicated the child's recorded brain-wave activity (EEG) and detailed brain structure, from connections between brain regions to the duration of synaptic plasticity—the changes that occur at the junctions through which neurons communicate. It also estimated possible patient-specific anomalies in synaptic transmission, consistent with the biological processes linked to ASD.

Building a digital twin one scan at a time

Computer models of the brain have become popular tools for showing how the brain's physical structure influences its activity. These models also have the potential to help doctors diagnose diseases and choose the best treatments for individual patients.

Advances in brain imaging now allow scientists to capture extremely fine details of the brain, such as myelination, the fatty layer that speeds up signal transmission along nerve fibers, as well as the physical properties of brain tissues like the skull and scalp, which shape how electrical signals travel through the brain.

Right now, however, the tools for modeling the brain are scattered. Imaging pipelines, electrical models of the head and brain-activity simulators each operate independently, with no single workflow connecting them. Unraveling the physiological pathways of ASD calls for a look at the bigger picture, including nuances in anatomical features and neural activity.

With FEDE, the researchers set out to make a unified system, so they started by taking advanced MRI scans to map the physical shape of the brain and EEG recordings to measure the child's real-life brain activity.

Mapping the connection between brain regions and myelination patterns gave them a detailed electrical model of the head. They also obtained a 3D computer model of the child's entire head, accounting for 12 different types of tissue, giving them a unique insight into the electrical signaling pathway from the surface to the depths.

The researchers used several layers of mathematical models to turn a toddler's MRI scans into a functioning virtual brain, capable of simulating brain waves and signaling activity.

The outcome was a detailed, tissue-aware model of the head for the digital twin that replicated the child's actual recorded brain waves (EEG) with high precision. It accurately captured both the timing of the brain activity and the specific locations where those signals originated. The team fine-tuned the model to match the child's brain activity, which revealed specific irregularities that were otherwise invisible.

The virtual brain showed random background noise in its electrical activity 100 times higher than that of a standard brain. Also, the brain's go-and-stop signals, also known as the excitation-to-inhibition (EI) ratio, were three times higher than normal, meaning the brain was overstimulated. These misfires are often found in individuals with ASD compared with those without the condition.

FEDE was far better at matching real-world brain activity than traditional virtual brain models. The team also noted that this was because most models fail to get brain signal speed right, as they do not account for myelination, which helps determine signal speed, unlike FEDE, which does include this parameter.

The results make clear that the ability to connect brain structure and activity can be achieved within a single model. According to the researchers, this could pave the way for more personalized investigations and treatments for brain disorders. However, the model must first be validated in larger and more diverse populations.

Written for you by our author Sanjukta Mondal, edited by Sadie Harley, and fact-checked and reviewed by Robert Egan—this article is the result of careful human work. We rely on readers like you to keep independent science journalism alive. If this reporting matters to you, please consider a donation (especially monthly). You'll get an ad-free account as a thank-you.

Publication details

Michelangelo Fabbrizzi et al, A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data, PLOS Digital Health (2026). DOI: 10.1371/journal.pdig.0001445

Journal information: PLOS Digital Health

Key medical concepts

Autism Spectrum DisorderElectroencephalographyMagnetic Resonance Imaging

Clinical categories

NeurologyPediatricsChildren's health Who's behind this story?

Sanjukta Mondal

Master's in Chemistry. Freelance science journalist and communicator. Published in Chemistry World, BioSpace, and The Hindu. Full profile →

Sadie Harley

BSc Life Sciences & Ecology. Microbiology lab background with pharmaceutical news experience in oil, gas, and renewable industries. Full profile →

Robert Egan

Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile →

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Citation: Anatomically accurate digital twin of 2-year-old's brain uncovers neural signatures linked to autism (2026, June 23) retrieved 23 June 2026 from https://medicalxpress.com/news/2026-06-anatomically-accurate-digital-twin-year.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.