Topic: Kinematic

Factors Driving Wear and Implant Failure in Total Shoulder Arthroplasty

Polyethylene (PE) wear and implant-related failure remain leading causes of revision in total shoulder arthroplasty (TSA), a procedure which now surpasses the growth rate of hip and knee arthroplasty. Both anatomic (aTSA) and reverse (rTSA) TSA outcomes are heavily influenced by complex interactions between rotator cuff function, scapular motion, implant design, and patient-specific loading—factors not adequately captured in current preclinical implant testing standards. Emerging evidence suggests that PE wear progression in TSA is highly dependent on shoulder kinematics, joint loading, implant positioning, and individual patient factors. Nonetheless, data on in vivo motion and load profiles remain sparse, and few tools exist to link these profiles to clinically relevant wear patterns or associated periprosthetic inflammatory tissue responses. Accordingly, the primary objective of this project is to develop validated, patient-specific models that predict PE wear in TSA and identify modifiable surgical, design, and rehabilitation targets to improve implant longevity and restore patient mobility. Additionally, we will establish histopathological hallmarks that indicate TSA failure caused by PE wear debris. Our central hypothesis is that specific shoulder kinematics and joint loading drive distinct PE wear patterns in TSA associated with mechanical failure or inflammatory-mediated osteolysis, depending on implant design and positioning. To achieve the overall objective of this work, shoulder motions and muscle excitations across 25 activities of daily living will be collected at pre-op and post-op (>6 months) in both aTSA and rTSA patients, with long-term follow-up of patient-reported outcomes via validated surveys (5 years). Unsupervised machine learning will categorize patients into movement-based phenotypes, which will then inform a multi-scale modeling framework to estimate in vivo shoulder joint loads and implant wear across the varying movement strategies. Predicted wear patterns will be validated using state-of-the-art preclinical wear simulators. Simultaneously, we will quantify how patient, surgical, and implant factors contribute to wear in retrieved TSA components (>400 samples), correlating imaging-based wear patterns with clinical outcomes, patient-reported function, inflammatory tissue responses, and radiographic indications of loosening. For that purpose, we will establish benchmarks of TSA wear rates and introduce a new histopathological approach augmented by infrared spectroscopic imaging. This work is innovative because we are linking patient-specific movement patterns following TSA with multi-scale computational models to predict PE wear, breaking the current approaches of using generic motions and loads in existing testing standards. This work will produce the first integrated, publicly available database of TSA kinematics, joint loading, and PE wear patterns and rates, along with validated computational tools to inform implant design, surgical planning, rehabilitation strategies, and personalized risk assessment. Ultimately, these advances will improve functional outcomes and long-term success for TSA patients and enable better preclinical testing methods and standards.

Role of Two Medial Prefrontal Long-Range Recurrent Networks in Behavior Initiation and Inhibition

Abstract The medial prefrontal cortex (mPFC) is critical for executive function, yet how its dorsal (dmPFC) and ventral (vmPFC) motor-projecting (MP) neurons coordinate behavioral initiation, inhibition, and cognitive flexibility remains poorly understood. This R21 leverages four translational behavioral paradigms (head-fixed Persistent Licking/Shock-Escape; freely moving FED3-based Reversal Learning/Stop-Signal), high-density neural recordings, circuit manipulations, and Brian2 spiking neural network modeling to test our central hypothesis: dmPFC MP neurons drive action initiation and adaptive switching, while vmPFC MP neurons suppress impulsivity and perseveration. In Aim 1a, we quantify behavior using kinematic analyses (jerk, velocity, z-scored) aligned with human executive dysfunction metrics (Action Latency [AL], Reversal Accuracy [RA], Perseveration Errors [PE], Stop-Signal Reaction Time [SSRT]), combined with optogenetic (stGtACR2/ChR2) and chemogenetic (PSAM/varenicline) perturbations. Aim 1b employs optotagging and population analyses (PCA, SVM, Total Spiking Probability Edges) to decode dmPFC/vmPFC MP dynamics across tasks, resolving specialized versus mixed functional roles. Aim 1c integrates these datasets into Brian2 spiking network models to predict neural-behavioral correlations, validated through cross-validation. Exploratory analyses will link murine kinematic signatures to human stop-signal/reversal learning metrics. By elucidating strain-specific (C57BL/6 vs. CD1) circuit mechanisms and delivering translatable biomarkers (AL, RA, PE, SSRT, kinematics), this work addresses a critical gap in understanding neuropsychiatric disorders like ADHD (impulsivity) and schizophrenia (perseveration). The study’s innovative combination of recurrent neural network theory, FED3-based assays, and New Approach Methodology (NAM)-compliant computational modeling pioneers high-risk, high-reward tools for circuit dissection, fully aligning with NIH’s 2025 priorities.

Mouse Motor Cortex Circuits and Roles in Oromanual Behavior

I’m interested in structure-function relationships in neural circuits and behavior, with a focus on motor and somatosensory areas of the mouse’s cortex involved in controlling forelimb movements. In one line of investigation, we take a bottom-up, cellularly oriented approach and use optogenetics, electrophysiology, and related slice-based methods to dissect cell-type-specific circuits of corticospinal and other neurons in forelimb motor cortex. In another, we take a top-down ethologically oriented approach and analyze the kinematics and cortical correlates of “oromanual” dexterity as mice handle food. I'll discuss recent progress on both fronts.

Multidimensional cerebellar computations for flexible kinematic control of movements

Cortex-dependent corrections as the mouse tongue reaches for and misses targets

Brendan Ito (Cornell University, USA) and Teja Bollu (Salk Institute, USA) share unique insights into rapid online motor corrections during mouse licking, analogous to primate goal-oriented reaching. Techniques covered include large-scale single unit recording during behaviour with optogenetics, and a deep-learning-based neural network to resolve 3D tongue kinematics during licking.

The role of high- and low-level factors in smooth pursuit of predictable and random motions

Smooth pursuit eye movements are among our most intriguing motor behaviors. They are able to keep the line of sight on smoothly moving targets with little or no overt effort or deliberate planning, and they can respond quickly and accurately to changes in the trajectory of motion of targets. Nevertheless, despite these seeming automatic characteristics, pursuit is highly sensitive to high-level factors, such as the choices made about attention, or beliefs about the direction of upcoming motion. Investigators have struggled for decades with the problem of incorporating both high- and low-level processes into a single coherent model. This talk will present an overview of the current state of efforts to incorporate high- and low-level influences, as well as new observations that add to our understanding of both types of influences. These observations (in contrast to much of the literature) focus on the directional properties of pursuit. Studies will be presented that show: (1) the direction of smooth pursuit made to pursue fields of noisy random dots depends on the relative reliability of the sensory signal and the expected motion direction; (2) smooth pursuit shows predictive responses that depend on the interpretation of cues that signal an impending collision; and (3) smooth pursuit during a change in target direction displays kinematic properties consistent with the well-known two-thirds power law. Implications for incorporating high- and low-level factors into the same framework will be discussed.

Analogies in motor learning - acquisition and refinement of movement skills

Analogies are widely used by teachers and coaches of different movement disciplines, serving a role during the learning phase of a new skill, and honing one’s performance to a competitive level. In previous studies, analogies improved motor control in various tasks and across age groups. Our study aimed to evaluate the efficacy of analogies throughout the learning process, using kinematic measures for an in-depth analysis. We tested whether applying analogies can shorten the motor learning process and induce insight and skill improvement in tasks that usually demand many hours of practice. The experiment included a drawing task, in which subjects were asked to connect four dots into a closed shape, and a mirror game, in which subjects tracked an oval that moved across the screen. After establishing a baseline, subjects were given an analogy, explicit instructions, or no further instruction. We compared their improvement in overall skill, accuracy, and speed. Subjects in the analogy and explicit groups improved their performance in the drawing task, while significant differences were found in the mirror game only for slow movements between analogy and controls. In conclusion, analogies are an important tool for teachers and coaches, and more research is needed to understand how to apply them for maximum results. They can rapidly change motor control and strategy but may also affect only some aspects of a movement and not others. Careful thought is needed to construct an effective analogy that encompasses relevant movement facets, as well as the practitioner’s personal background and experience.

Neural mechanisms of proprioception and motor control in Drosophila

Animals rely on an internal sense of body position and movement to effectively control motor behaviour. This sense of proprioception is mediated by diverse populations of internal mechanosensory neurons distributed throughout the body. My lab is trying to understand how proprioceptive stimuli are detected by sensory neurons, integrated and transformed in central circuits, and used to guide motor output. We approach these questions using genetic tools, in vivo two-photon imaging, and patch-clamp electrophysiology in Drosophila. We recently found that the axons of fly leg proprioceptors are organized into distinct functional projections that contain topographic representations of specific kinematic features: one group of axons encodes tibia position, another encodes movement direction, and a third encodes bidirectional movement and vibration frequency. Whole-cell recordings from downstream neurons reveal that position, movement, and directional information remain segregated in central circuits. These feedback signals then converge upon motor neurons that control leg muscles during walking. Overall, our findings reveal how a low-dimensional stimulus – the angle of a single leg joint – is encoded by a diverse population of mechanosensory neurons. Specific proprioceptive parameters are initially processed by parallel pathways, but are ultimately integrated to influence motor output. This architecture may help to maximize information transmission, processing speed, and robustness, which are critical for feedback control of the limbs during adaptive locomotion.

ON THE CONTRIBUTION OF KINEMATIC AND SOCIAL CUES TO THE PERCEPTION OF MOVEMENT VIGOR

FENS Forum 2026

Action recognition and kinematic analysis with DeepLabCut

Intersecting kinematic encoding and readout of intention in autism

Movement Direction and Joint Kinematics Define Trajectory Variability Patterns

Multidimensional cerebellar computations for flexible kinematic control of movements

Purkinje cell microzones mediate distinct kinematics of a single movement

Simultaneous yet separable population encoding of arm movement direction and kinematics in motor cortex

Spatio-temporal dependency of striatal dopamine in the control of movement kinematics in rats