Biology subjects

Ietswaart, M.

Publications and source records attributed to Ietswaart, M..

4 recordsLinked to original sources

bioRxiv2026

Environmental Demands Outweigh Age Effects in Cortical Dynamics During Walking

Reduced visibility is a common contributor to falls in older adults, but the neural processes that support safe locomotion under low lighting are not well established. In this study, 22 young (mean age = 22.5) and 20 older (mean age = 70.9) adults walked along a pathway under bright, ambient and dark lighting conditions while stepping over expected and unexpected obstacles projected on the floor. Mobile electroencephalography (EEG), combined with motion capture, was used to quantify spectral power changes during obstacle crossing preparation and the post-crossing phases, relative to a standing baseline. We identified that reduced lighting conditions elicited a widespread increase in cortical engagement, predominantly in the alpha band, during both preparation and reset phases. Obstacle negotiation recruited a distributed sensorimotor network, with reduced theta-alpha-beta desynchronisation during preparation and a robust post-movement beta rebound during the post-crossing reset phase. Expectation effects were modest, with unexpected obstacles inducing greater transient alpha-beta desynchronisation relative to expected obstacles, suggesting additional cortical resources are recruited under uncertainty. Age effects were minimal and limited to reduced frontal alpha desynchronisation in older adults compared to young adults during the preparation phase, possibly reflecting differences in walking speed rather than differences in neural control. In conclusion, environmental features such as low lighting and obstacle presence exert a stronger influence on cortical dynamics during walking than age and expectation effects alone. This provides a foundation for future work examining how these processes may differ in older adults at increased risk of falls.

neuroscience↗

bioRxiv2026

Acute Temporal Dynamics of Brain Injury Plasma Biomarkers following Controlled Football Heading

Footballers are routinely exposed to repeated non-concussive head impacts with (soccer) heading, the acute neurobiological consequences of which remain poorly characterised. Current evidence for acute changes in biofluid markers following heading is tempered by design limitations including inadequate impact exposure monitoring, uncontrolled confounders (e.g. exercise), the lack of a control group and heterogeneity in sampling times. This study attempts to address these limitations with a novel combination of serial sampling measuring biomarkers across five timepoints (pre and 0.5, 2, 4 and 24 h post heading), a within-subject, time-matched resting control condition and participant-level finite element modelling used for impact quantification. In a counter-balanced crossover design, twelve male football players aged 18-31 completed a heading session with 10 verified rotational headers delivered from a ball launcher at 10 m and a control condition. Impact kinematics were captured with instrumented mouthguards and modelled using the Edinburgh finite element head model (EdiFEHM). Additional monitoring measures included near point convergence (NPC) at each timepoint and the sport concussion assessment tool (SCAT6) at the pre, 0.5 and 24 h timepoints. The Quanterix Simoa N4PD Advantage Plus assay was used to quantify plasma concentration of neurofilament light (NfL), glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and brain-derived tau (BD-tau) measured for the first time in this context. GFAP and BD-tau showed significant main effects of time (both P < 0.005) but no effect of heading. UCH-L1 was excluded and NfL restricted to descriptive analysis as both fell near or below the minimum assay quantification level. NPC alongside SCAT6 symptom and cognitive scores remained unaffected by heading. Tissue strain modelling showed a mean peak MPS95 of 0.099 (range: 0.074-0.144) consistent with low magnitude non-concussive impact exposure across participants. These findings provide a methodological framework and anchor point for non-concussive impact research employing biofluid sampling while highlighting sensitivity limitations of current multiplex Simoa assays in certain biomarkers for healthy young-adult populations. Design elements including time-matched controls, serial sampling and individualised mechanical exposure outcomes are essential for characterising exposure and avoiding spurious findings. The study was preregistered with the ISRCTN registry (ISRCTN44241334).

neuroscience↗

bioRxiv2025

A meta-analysis of periodic and aperiodic M/EEG components in Parkinson's disease

Parkinsons disease is characterised by a range of motor and non-motor changes that can negatively impact quality of life. Many studies have identified potential clinical electrophysiological biomarkers of Parkinsons disease with an aim of developing new methods of identifying at-risk patients, and to form the basis of therapeutic interventions. However, these studies do not present consistent results, and a formal meta-analysis is warranted to identify reliable M/EEG characteristics across datasets. In this meta-(re-)analysis of open-access M/EEG datasets (n = 6; 4 EEG and 2 MEG), we compared periodic and aperiodic characteristics of resting-state recordings in 368 patients with Parkinsons disease and 570 age-matched healthy controls. Specifically, we compared the power and peak frequency of the aperiodic-adjusted alpha and beta oscillations, and the aperiodic exponent and offset across the two groups. Using spectral parametrisation, individuals with Parkinsons disease had higher alpha-band power and a slower alpha peak frequency compared to controls, however no group differences in beta-band power and peak frequency were identified in this resting state data. Parkinsons patients were furthermore found to have consistently higher aperiodic offset and exponent, possibly indicative of increased cortical inhibition. In conclusion, this large cohort meta-analysis points to a broadly consistent pattern of both periodic and aperiodic changes in Parkinsons patients in M/EEG signal that may be used to develop diagnostics and targeted interventions in the future.

neuroscience↗

bioRxiv2020

Mobile EEG reveals functionally dissociable dynamic processes supporting real-world ambulatory obstacle avoidance: Evidence for early proactive control

The ability to safely negotiate the world on foot takes years to develop in human infants, reflecting the huge cognitive demands associated with real-time dynamic planning and control of walking. Despite the importance of walking, surprisingly little is known about the neural and cognitive processes that support ambulatory motor control in humans. In particular, methodological limitations have, to date, largely prevented study of the neural processes involved in detecting and avoiding obstacles during walking. Here, using mobile EEG during real-world ambulatory obstacle avoidance, we captured the dynamic oscillatory response to changes in the environment. Time-frequency analysis of EEG data revealed clear neural markers of proactive and reactive forms of movement control (occurring before and after crossing an obstacle), visible as increases in frontal theta and centro-parietal beta power respectively. Critically, the temporal profile of changes in frontal theta allowed us to arbitrate between early selection and late correction mechanisms of proactive control: our data show that motor plans are updated as soon as an upcoming obstacle appears, rather than when the obstacle is reached, as previously thought. In addition, regardless of whether motor plans required updating, a clear beta rebound was present after obstacles were crossed, reflecting the resetting of the motor system. Overall, our use of mobile EEG during real-world walking provides novel insight into the cognitive and neural basis of dynamic motor control in humans, suggesting new routes to the monitoring and rehabilitation of motor disorders such as dyspraxia and Parkinsons disease.

neuroscience↗