Summary: Researchers have identified specific, microscopic gait patterns associated with Fragile X syndrome and SHANK3 deletion syndrome—two genetic conditions linked to autism—by analyzing subtle movements recorded by motion-sensored footwear.
Source: Rutgers University
Rutgers researchers report that Fragile X and SHANK3 deletion syndromes, both associated with autism and related neurological concerns, show distinct signatures in walking behavior when examined at a microscopic level using motion-sensored sneakers.
Published in Scientific Reports, the study demonstrates a technique that can detect early gait abnormalities 15 to 20 years before traditional clinical diagnosis, offering promise for earlier intervention strategies to help preserve brain structure and function.
“Walking carries important information about nervous system health, but subtle gait abnormalities—especially those linked to conditions like Fragile X—can remain invisible to the naked eye for many years,” said Elizabeth Torres, coauthor of the study, professor of psychology at Rutgers University–New Brunswick and director of the Sensory Motor Integration Lab. “Differences in body proportions, age, and developmental stage make it difficult to use gait as a broad screening tool. Our approach addresses those challenges by focusing on microvariations in movement rather than crude averages.”
Fragile X carrier rates are notable: the National Fragile X Foundation estimates about 1 in 468 men and 1 in 151 women carry the gene responsible for Fragile X syndrome. Detecting SHANK3 deletions can also be difficult; the National Organization for Rare Disorders reports that more than 30 percent of affected individuals require multiple chromosome studies before the deletion is identified, with an estimated prevalence of 2.5 to 10 cases per million births and equal likelihood among males and females.
In this study, the team analyzed subtle, otherwise invisible walking movements from 189 participants to identify signatures of nervous system pathology. The movements were captured with wearable motion-sensored sneakers developed in collaboration with researchers at Stevens Institute of Technology, and the datasets were augmented with video recordings, heart rate measures and wearable devices such as fitness trackers.
Rather than relying on averaged measures that wash out brief fluctuations, the researchers focused on micro-fluctuations—spikes, valleys and neighboring points—in the continuous stream of motion data. Using statistical techniques developed by Torres and causal forecasting models devised by Rutgers graduate student Theodoros Bermperidis, the team identified important time lags and causal relationships among these microvariations. This approach reveals how the timing and interaction of tiny movement features distinguish healthy gait from early-stage pathology.
By combining non-linear causal network analyses across time and frequency domains with stochastic mapping, the method creates a framework to predict when an individual’s walking pattern begins to diverge from typical development. The approach can stratify a diverse cohort—mixing healthy controls across ages with patients carrying neurological conditions—into meaningful clusters that reflect distinct gait pathologies and their likely progression.
“Because Fragile X and SHANK3-related conditions overlap with other neurological disorders such as autism, Fragile X-associated tremor/ataxia syndrome and even Parkinson’s, detecting early, abnormal gait signatures provides an important screening avenue,” said lead author Theodoros Bermperidis.
The research also reaffirms that gait changes naturally with age, with hip, knee and ankle joints and the thigh, leg and foot bones typically showing the earliest effects of aging on walking. Distinguishing these normal age-related changes from early pathological signs is a critical clinical challenge.
Clinicians often encounter patients whose walking pattern appears unusual on first examination. Torres emphasizes that combining wearable biosensors with advanced analytics and clinical expertise can reveal subtle disease signatures that are otherwise undetectable at routine visits, enabling earlier and more personalized care.
Co-authors of the study include Richa Rai (Rutgers graduate student), Jihye Ryu (former Rutgers student) and collaborators from Stevens Institute of Technology, Columbia University, Columbia University Medical Center, NewYork-Presbyterian/Columbia University Irving Medical Center and Columbia University College of Physicians and Surgeons.
Funding: The research received support from the New Jersey Governor’s Council for the Medical Research and Treatment of Autism and the Nancy Lurie Marks Family Foundation Career Development Award to Elizabeth B. Torres.
About this genetics research news
Author: Megan Schumann
Source: Rutgers University
Contact: Megan Schumann – Rutgers
Image: The image is in the public domain
Original Research: Open access. “Optimal time lags from causal prediction model help stratify and forecast nervous system pathology” by Theodoros Bermperidis, Richa Rai, Jihye Ryu, Damiano Zanotto, Sunil K. Agrawal, Anil K. Lalwani & Elizabeth B. Torres. Scientific Reports
Abstract
Optimal time lags from causal prediction model help stratify and forecast nervous system pathology
Conventional clinical diagnosis of nervous system disorders relies on standardized observational criteria that aim to unify symptom descriptions, but this often yields highly heterogeneous diagnostic groups. A key question is how to automatically stratify a random cohort of the population so that treatments can be matched to each cluster and the future course of a group forecasted to enable preventive, neuroprotective therapies.
This study introduces methods for automatic stratification that combine measurements of micro-fluctuations in biorhythmic motion during a simple walking task with non-linear causal network connectivity analyses in both temporal and frequency domains, together with stochastic mapping. The resulting internal motor timing metrics support the identification of self-emerging clusters that reflect fundamentally different gait pathologies and enable personalized intervention strategies.
Framed around the principle of reafference and operationalized through causal prediction models, these findings extend the theory of internal models in neuromotor control and provide practical tools for early detection and forecasting of nervous system pathology based on wearable sensor data.