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Background: Motor difficulties are common in many, but not all, autistic individuals. These difficulties can co-occur with other problems, such as delays in language, intellectual, and adaptive functioning. Biological mechanisms underpinning such difficulties are less well understood. Poor motor skills tend to be more common in individuals carrying highly penetrant rare genetic mutations. Such mechanisms may have downstream consequences of altering neurophysiological excitation-inhibition balance and lead to enhanced behavioral motor noise.
Methods: This study combined publicly available and in-house datasets of autistic (n = 156), typically-developing (TD, n = 149), and developmental coordination disorder (DCD, n = 23) children (age 3-16 years). Autism motor subtypes were identified based on patterns of motor abilities measured from the Movement Assessment Battery for Children 2nd edition. Stability-based relative clustering validation was used to identify autism motor subtypes and evaluate generalization accuracy in held-out data. Autism motor subtypes were tested for differences in motor noise, operationalized as the degree of dissimilarity between repeated motor kinematic trajectories recorded during a simple reach-to-drop task.
Results: Relatively 'high' (n = 87) versus 'low' (n = 69) autism motor subtypes could be detected and which generalize with 89% accuracy in held-out data. The relatively 'low' subtype was lower in general intellectual ability and older at age of independent walking, but did not differ in age at first words or autistic traits or symptomatology. Motor noise was considerably higher in the 'low' subtype compared to 'high' (Cohen's d = 0.77) or TD children (Cohen's d = 0.85), but similar between autism 'high' and TD children (Cohen's d = 0.08). Enhanced motor noise in the 'low' subtype was also most pronounced during the feedforward phase of reaching actions.
Limitations: The sample size of this work is limited. Future work in larger samples along with independent replication is important. Motor noise was measured only on one specific motor task. Thus, a more comprehensive assessment of motor noise on many other motor tasks is needed.
Conclusions: Autism can be split into at least two discrete motor subtypes that are characterized by differing levels of motor noise. This suggests that autism motor subtypes may be underpinned by different biological mechanisms.
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http://dx.doi.org/10.1186/s13229-024-00618-0 | DOI Listing |
Neuroimage
September 2025
Center for Bioelectric Interfaces, Higher School of Economics, Moscow, Russia; LLC "Life Improvement by Future Technologies Center", Moscow, Russia; AIRI, Artificial Intelligence Research Institute, Moscow, Russia. Electronic address:
Objective: Upcoming neuroscientific research will require bidirectional and context dependent interaction with nervous tissue. To facilitate the future neuroscientific discoveries we have created HarPULL, a genuinely real-time system for tracking oscillatory brain state.
Approach: The HarPULL technology ensures reliable, accurate and affordable real-time phase and amplitude tracking based on the state-space estimation framework operationalized by Kalman filtering.
Objective: Effective deep brain stimulation (DBS) treatment for Parkinson's disease requires careful adjustment of stimulation parameters and targeting to avoid motor side effects caused by activation of the internal capsule. Currently, patients must self-report side effects during device programming and implantation surgery - a challenging and subjective process that could lead to suboptimal therapy or exacerbate the time needed to optimize treatment. Motor evoked potentials (mEP), the use of electromyography to record DBS-induced muscle activation, offer a promising biomarker for objective motor side effect detection.
View Article and Find Full Text PDFUnlabelled: Adaptive behavior requires integrating information from multiple sources. These sources can originate from distinct channels, such as internally maintained latent cognitive representations or externally presented sensory cues. Because these signals are often stochastic and carry inherent uncertainty, integration is challenging.
View Article and Find Full Text PDFJ Appl Physiol (1985)
September 2025
Department of Neuroscience, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
This study aimed to characterize motor noise in human standing balance and uncover mechanisms that enable the nervous system to robustly sense and control upright posture despite this variability. We conducted three experiments using a robotic balance simulator. First, we quantified the natural variability of ankle torques, revealing that torque variability was stable within preferred postures and increased only at more extreme orientations.
View Article and Find Full Text PDFNeural Regen Res
September 2025
Department of Biomedical Engineering, Tianjin University School of Medicine, Tianjin, China.
Electroencephalography-based brain-computer interfaces have revolutionized the integration of neural signals with technological systems, offering transformative solutions across neuroscience, biomedical engineering, and clinical practice. This review systematically analyzes advancements in electroencephalography-based brain-computer interface architectures, emphasizing four pillars, namely signal acquisition, paradigm design, decoding algorithms, and diverse applications. The aim is to bridge the gap between technology and application and guide future research.
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