AI Links Brain Rhythms to Physical “Wiring” Across Lifespan

 What is your competent? doctors' EXACT PROTOCOL TO ENSURE YOUR WHITE MATTER WIRING AND MYELIN IS WORKING PROPERLY?

Nothing, like usual!

demyelinating (24 posts to May 2012)

demyelination (12 posts to November 2021)

AI Links Brain Rhythms to Physical “Wiring” Across Lifespan

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.

The Xi–αNET model demonstrates that brain rhythms are reflections of the brain’s physical wiring and the efficiency of its signal highways. Credit: Neuroscience News

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:

Q: Why do our brain waves slow down as we get older?

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.

Q: Can an EEG now tell me my “brain age”?

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.

Q: What is the difference between “background noise” and “alpha waves” in the brain?

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

The 3 Forms of Sleep Disruption That Shrink Your Brain—And How to Tell If Your Sleep Is Actually Protecting You From Cortical Atrophy, Brain Shrinkage and Neurodegeneration

 Does your competent? doctor even have a sleep protocol? In my quad in the hospital almost every morning at 7am the vampires came to draw blood from someone. Sleep quality obviously wasn't considered at all. At 10pm sleeping pills were handed out like candy, do sleeping pills even provide the proper sleep?

The 3 Forms of Sleep Disruption That Shrink Your Brain—And How to Tell If Your Sleep Is Actually Protecting You From Cortical Atrophy, Brain Shrinkage and Neurodegeneration

You’ve probably heard of memory loss or brain fog.

But what about brain shrinkage?

MRI studies show that even in healthy, high-performing adults, the brain starts to lose volume—especially in regions tied to focus, planning, and emotional regulation—as early as your 30s and 40s.

The cortex starts to thin.
Frontal regions lose volume.

And while aging plays a role, there’s another, less obvious driver behind these early changes: sleep quality.

Poor sleep was associated with advanced brain age in midlife.

— Dr. Clémence Cavaillès, UCSF, 2024

New research shows that poor sleep fragmentation, poor REM sleep, or misaligned sleep may be one of the most underrecognized causes of brain shrinkage in midlife.

And it doesn’t take extreme deprivation to matter.

Even subtle disruptions can lead to measurable cortical atrophy over time—undermining both cognitive longevity (your capacity to think, focus, and sustain brain health over decades) and performance longevity (your ability to maintain physical, mental, and emotional output as you age).

This article examines how poor sleep contributes to measurable brain shrinkage—with MRI evidence from clinical and population studies—and defines what truly restorative sleep looks like, according to sleep medicine standards.

It also shows why most consumer sleep trackers miss the biological signals that matter most for long-term brain health.

➤ How fragmented or REM-poor sleep accelerates structural brain decline

➤ Which regions are affected—and how early the changes begin

➤ What defines restorative sleep (hint: it’s not your ring score)

➤ The overlooked sleep disruptor most people never check for

Table of Contents

Section 1: Poor Sleep Quality Triggers Cortical Atrophy, Brain Shrinkage and Brain Aging

• ➤ The UCSF/CARDIA Study – Poor Midlife Sleep and Brain Shrinkage on MRI
• ➤ Neurology 2014 – Cortical Atrophy Tracks with Poor Sleep Efficiency

Section 2: REM Sleep Loss and Brain Shrinkage in Alzheimer’s-Prone Regions

• ➤ Brain Shrinkage Causes in Alzheimer’s-Vulnerable Regions: REM Sleep

Your Sleep Score Isn’t the Whole Story.

Section 3: Night-Time Awakenings and Cortical Atrophy (Cortical Thinning)

• ➤ The 2016 Sleep Study – Sleep Fragmentation and Frontal Cortical Atrophy
• ➤ Why Sleep Fragmentation Is Easy to Miss—and Hard to Repair

Fragmented sleep isn’t always a habit issue.

Section 4: What Good Sleep Really Means (And How to Know If You’re Getting It)

• ➤ Your Sleep Ring Isn’t Telling You Everything

Section 5. Still Waking Up in the Middle of the Night? The One Sleep Factor 99% of People Still Miss

• ➤ What Wearables Miss: Invisible Inputs That Disrupt Sleep
• ➤ The Inputs That Fragment Sleep—And How to Track Them in Real Time
• Explore the Airthings Wave Enhance here.

Frequently Asked Questions (FAQ)

• What is brain atrophy?
• What causes brain atrophy?
• Can lack of sleep cause brain atrophy?
• What is sleep fragmentation?
• What are the effects of fragmented sleep?
• How to reverse brain shrinkage?

References

Cognitive-motor dual-task training on gait and balance in stroke patients: meta-analytic report and trial sequential analysis of randomized clinical trials

 Can your competent/ doctor get this protocol to try on you even if it is of low quality? Or can't your doctor even manage that simple task?

Cognitive-motor dual-task training on gait and balance in stroke patients: meta-analytic report and trial sequential analysis of randomized clinical trials

• Lu Zhang,
• Jiangping Ma,
• Xiaoqing Liu,
• Aiping Jin,
• Kai Wang &
• Xiaobing Yin 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 227 (2024) Cite this article

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Abstract

Objective

Cognitive-motor dual-tasking training (CMDT) might improve limb function and motor performance in stroke patients. However, is there enough evidence to prove that it is more effective compared with conventional physical single-task training? This meta-analysis and Trial Sequential Analysis of randomized clinical trials (RCTs) aimed to evaluate the effectiveness of CMDT on balance and gait for treating hemiplegic stroke patients.

Methods

The databases were searched in PubMed, Web of Science, Ovid Database and The Cochrane Library, SinoMed database, Chinese National Knowledge Infrastructure (CNKI), Wan Fang database, and VIP database up to December 8, 2023. The Cochrane-recommended risk of bias (RoB) 2.0 tool was employed to assess risk of bias in trials. The statistical analysis was employed using R version 4.3.2. In addition, subgroup analyses and meta-regression were performed to explore the possible sources of heterogeneity. The evidence for each outcome was evaluated according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. The Copenhagen Trial Unit's Trial Sequential Analysis (version 0.9.5.10 Beta) was used for sequential analysis.

Results

Seventeen randomized clinical trials (RCTs) (n = 751 patients) were included. The results demonstrated that cognitive-motor dual-task training (CMDT) might be beneficial on stroke patients on Berg Balance Scale (BBS) (MD = 4.26, 95% CI 1.82, 6.69, p < 0.0001) (low-quality evidence). However, CMDT might not affect Time Up and Go test (TUG) (MD = −1.28, 95% CI −3.63, 1.06, p = 0.284); and single-task walking speed (MD = 1.35, 95% CI −1.56, 4.27, p = 0.413) in stroke patients (low-quality evidence). The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) results indicated that all findings were very low to low certainty. Trial Sequential Analyses demonstrated larger sample sizes are required for confirming our findings.

Conclusion

Cognitive-motor dual-task training (CMDT) compared with conventional physical single-task training might be an effective intervention for improving static balance function in stroke patients (low-quality evidence), which should be interpreted cautiously due to heterogeneity and potential biases. Nevertheless, further research is required to support the abovementioned findings.

Trial Registration This protocol was registered in PROSPERO (CRD42023490530).

Introduction

Stroke is an acute cerebrovascular disease that can cause cognitive, motor, and balance dysfunctions [9, 77]. These dysfunctions can significantly impact the patient's quality of life and are leading causes of disability and death [44]. Motor dysfunction that affects the ability to walk is a significant factor in the reintegration of stroke survivors into social activities. Evidence-based medicine confirms that early post-stroke rehabilitation is an effective method to reduce disability rates and improve patients' limb dysfunction [13]. Stroke patients are typically treated with early rehabilitation under single-task (ST) conditions [69]. This approach improves patients' limb function [69]. However, research has shown that only 60%-80% of stroke patients who have undergone single-task training can walk independently [69]. Additionally, A significant proportion of patients continue to exhibit reduced gait function and an increased risk of falls following their discharge from the hospital.

Cognitive-motor dual-task training (CMDT) involves performing cognitive tasks alongside motor training [70, 85, 87], a novel rehabilitation tool to help stroke patients. Studies of the neural bases of the effects of CMDT have shown that there was an increase in brain activity during dual-task (DT) especially in the pre-frontal cortex (PFC) [5, 36]. A meta-analysis of thirteen studies that utilized fNIRS to investigate cognitive challenges during dynamic balance control found that dual-tasking resulted in increased pre-frontal cortex activation compared with single-tasking [79]. It achieves this by accelerating central neural transduction, activating the higher cortex of the brain, optimizing the allocation of attentional resources, and facilitating neurological remodeling, which simulates a real-life environment for rehabilitation in both motor and cognitive domains [71, 75, 81]. Motor training is thought to promote synaptic plasticity and cell proliferation. In contrast, cognitive training seems to direct these newborn neurons into connection with pre-existing neural networks [6, 18, 25], which can increase the speed of information processing. CMDT can effectively strengthen the functional network connections between cognitive and motor regions, activating the cerebral cortex and facilitating the remodeling of brain functional networks. [56] CMDT enables the reorganization of cognitive task allocation strategies, optimizes the allocation of cognitive resources, increases coordination between tasks, and increases the flexibility of resource allocation [11].

There are three main underlying theories of cognitive-motor dual-task training (CMDT): the bottleneck, the cross-talk, and the capacity-sharing theory. The bottleneck theory indicates that encompassing the process of task training is sequential, not parallel [58]. the cross-talk theory postulates that if two tasks are from the same cognitive domain and neuronal populations in the brain, they will not interfere with each other [52]. the capacity-sharing theory postulates that humans have limited cognitive capacity and that doing two tasks simultaneously decreases performance on one or both [27].

Cognitive functions include attention, working memory, and executive ability. The interaction between these two executive functions, working memory and attention, could promote neurological rehabilitation outcomes. Hard cognitive tasks (HC) distracted more attention and reduced attention to conscious postural control in stroke patients [28]. One possible explanation may be that cognitive tasks require more complex mental processes such as working memory, mental tracking, and decision-making [2]. Working memory tasks are designed to retain things in the mind to perform complex tasks such as reasoning, understanding, and learning [3]. It has been found that working memory requires increased presynaptic glutamate release and changes in postsynaptic glutamate receptor activity [65]. The bottleneck theory assumes that all tasks involving stimulus–response associations depend on a central processor, i.e., only one task can be processed at a given moment, while the other waits, i.e., the central processing stages of the two tasks cannot overlap. This means that the central processing stages of the two tasks cannot overlap, thus creating a central processing bottleneck in the secondary task. Although the bottleneck theory emphasizes that the tasks are processed in a strict serial order, and this serial processing model will include some primitive requirements and possibly additional processing demands, it has also been found that these additional mental processes are closely related to working memory [53]. Attention is the ability of an individual to focus and concentrate on perception, thought, and behavior selectively. [66].Attention is often considered to be the basis of cognitive functioning. The capacity-sharing theory suggests that two tasks can run in parallel but compete for limited processing resources, resulting in reduced performance. The extent to which a single task is affected during dual-tasking ultimately depends on how one allocates attention to the corresponding task, so we must match appropriate attentional resources to each task [68]. During dual-task training, the decline in cognitive or motor performance ability in the cognitive-motor dual-task training (CMDT) group at the beginning, which gradually diminished with the prolongation of the treatment time, may suggest that the patients were progressively able to allocate their attentional resources appropriately during repeated training. So that the speed of synaptic signaling of brain neurons is accelerated, attention and executive function can be significantly improved [42].

After comparing the capacity-sharing theory with the bottleneck theory, it is easy to find that the two theories have different focuses. The former believes that there is sharing in multitasking and that tasks can be processed simultaneously so that attentional resources can be allocated appropriately. The latter, on the other hand, believes that there is no sharing in task processing and that tasks are processed in a strict order of priority, which also requires further research to clarify the mechanism of multitasking.

A previous meta-analysis demonstrated that cognitive-motor dual-task training (CMDT) improves balance, gait, and upper limb function in patients with chronic-phase stroke. However, the sample size was relatively small, and the source of heterogeneity was not explored. Performing dual tasks requires more cognitive aspects including attention and working memory [83], which requires meta-analyses to explore whether different elements of cognitive domains impact neurorehabilitation to meet the mental needs of stroke patients. To further elucidate the benefits of CMDT on balance and gait function in stroke patients, this study evaluated the clinical efficacy of CMDT based on moderator analysis and Trial Sequential Analysis (TSA) [78]. Furthermore, the study aimed to determine the necessary sample size.

A lack of precision characterizes the results of meta-analyses with sparse data. Such meta-analyses are typically updated periodically to obtain additional experimental data, necessitating repeated significance tests. The repetition of tests on accumulating data increases the overall risk of a type 1 error occurring. Applying Trial Sequential Analysis necessitates meticulous consideration of statistical significance thresholds, trial size, heterogeneity, and potential random errors. This approach necessitates the calculation of the requisite information to ascertain the optimal sample size required to determine a specific effect size and achieve a specified level of statistical power. Trial Sequential Analysis (TSA) is a novel tool that can reduce the risk of inflated type one error to verify the robustness of the findings [78]. Therefore, to extend previously available evidence, this study employed meta-analysis to evaluate and analyze the results of randomized controlled clinical trials of cognitive-motor dual-task training (CMDT) applied to post-stroke gait and balance disorders published by December 8, 2023. The aim was to provide a basis for the future clinical practice of cognitive-motor dual-task training in gait and balance recovery in stroke patients.

Impact of chronic ankle instability on gait loading strategy in individuals with chronic ankle instability: a comparative study

 In stroke our chronic ankle problem is ankle rolling; WHAT IS YOUR COMPETENT? DOCTOR'S EXACT PROTOCOL TO ADDRESS THAT? NONE? So, you don't have a functioning stroke doctor, do you? 

Impact of chronic ankle instability on gait loading strategy in individuals with chronic ankle instability: a comparative study

• Omar M. Elabd,
• Aliaa M. Elabd,
• Mona S. Abd El-Azez,
• Mohamed M. Taha &
• Amira H. Mohammed 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 185 (2024) Cite this article

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Abstract

Background

Lateral ankle sprains rank among the most prevalent musculoskeletal injuries, while chronic ankle instability (CAI) is its most common cascade. In addition to the conflicting results of the previous studies and their methodological flaws, the specific gait loading strategy is still not well studied.

Purpose

The study aimed to investigate the fluctuations in gait loading strategy in people with chronic ankle instability compared to health control.

Methods

A total of 56 male subjects participated in this study and were allocated into two groups: (A) CAI group: 28 subjects with unilateral CAI (age 24.79 ± 2.64 and BMI 26.25 ± 3.50); and (B) control group: 28 subjects without a history of ankle sprains (age 24.57 ± 1.17 and BMI 26.46 ± 2.597). Stance time, weight acceptance time, and load distribution were measured to investigate gait loading strategy.

Results

The study findings revealed that the CAI group had a significant higher load over the lateral rearfoot. However, MANOVA indicates that there was no overall significant difference in gait loading strategy between the CAI and control groups. Furthermore, in terms of stance time, time of weight acceptance phase, load over medial foot, and load over lateral foot, CAI and healthy controls seemed to walk similarly.

Conclusions

The findings revealed that individuals with CAI had the significant alteration in the lateral rearfoot loading, suggesting a potential compensatory mechanism to address instability during the weight acceptance phase. This could manifest a laterally deviated center of pressure and increased frontal plane inversion during the early stance phase. However, it is acknowledged that these alterations could be both the result and the origin of CAI. The study highlights the vulnerability of CAI during the early stance phase, emphasizing the need for gait reeducation as individuals return to walking as healthcare clinicians should focus on treatment modalities aimed at reducing rearfoot inversion in individuals with CAI.

Introduction

Lateral ankle sprains rank among the most prevalent musculoskeletal injuries in both athletes and non-athletes [1, 2], while chronic ankle instability (CAI) is its common cascade, as approximately 40% of those affected go on to develop CAI [3,4,5]. The defining features of CAI encompass recurring ankle sprains, pain, ankle muscle weakness, limited ankle motion, and a subjective sensation of the ankle giving way, remaining for at least one year post-injury [4, 6].

Both sensorimotor and mechanical impairments could result in CAI [4, 6]. Mechanical factors involve ligamentous dysfunction due to hyperlaxity, as well as restrictions in arthrokinematics and osteokinematics [7,8,9]. This can also manifest even in the absence of mechanical constraints at the ankle [10]. While sensorimotor factors involve altered somatosensation, joint position sense, and reflexes, pain, ankle muscle weakness, reduced ankle range of motion (ROM), and impaired postural control [10,11,12,13,14,15,16,17].

Chronic ankle instability could result in negative health consequences such as diminished physical activity, altered movement patterns in tasks like walking, jogging, and turning, a higher risk of falls due to impaired postural control, and a higher incidence of posttraumatic ankle osteoarthritis. Also, individuals with CAI often experience functional limitations affecting daily activities, leading to poor quality of life, so CAI is a major public health issue [6, 18,19,20,21,22,23,24,25,26,27].

Gait alterations have been documented in CAI, and most of the studied parameters were spatiotemporal ones. Step length, cadence, walking speed, and single limb duration were reduced in those with CAI, while their base of support was larger. These changes in gait could be the result of patients adopting a modified gait to make up for their sensation of instability [28, 29]. Thus, these changes might have a detrimental impact on neuromuscular strategies and motor performance [30, 31].

Individuals with chronic ankle instability show altered regional activation of the peroneus longus muscle.

Researchers’ attention has been drawn to these altered neuromuscular strategies during walking, such as changes in activation patterns of ankle muscles, altered ankle kinematics, and variability in location of the center of pressure (COP), but their findings were inconsistent [3, 32,33,34]. In the proneus longus, tibialis anterior, and gastrocnemius, some research has found a reduction in their activity [34], while other studies have observed greater activation in the same muscles [33]. However, a recent study revealed that individuals with chronic ankle instability show altered regional activation of the peroneus longus muscle [35]. In ankle kinematics and location of COP during a stance phase, greater ankle inversion and a lateral deviation of the COP have been reported [26]. On the other hand, greater inversion was observed when running but not when walking [27]. Also, previous studies had methodological flaws due to the inclusion of subjects with bilateral CAI, ignoring that one limb influences the other and could lead to an gait alterations [36].

Besides these conflicting results and methodological flaws, the specific gait loading strategy is still not well studied. As far as we are aware, no research has thoroughly investigated how CAI affects the biomechanical aspects of gait loading strategy. In order to fill in the knowledge gap left by earlier studies and accurately specify gait loading strategy for this population, the current study used a more thorough and appropriate design that took into account the homogeneity of the sample, the elimination of bilateral CAI, and the existence of healthy controls. The study’s objectives were to investigate the changes in gait loading strategy in CAI compared to health control, hypothesizing that there was a significant difference in loading strategy between both groups. Understanding how CAI affects gait loading strategy can help design interventions to restore normal loading patterns and reduce injury risk. This knowledge can help clinicians to develop targeted rehabilitation programs, improve rehabilitation effectiveness, reduce risk of fall, and avoid complications, and improve quality of life for individuals with CAI, thereby reducing the risk of further injuries.