Name: A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease (Video) | JoVE Journal
Uploaded: 2025-06-13T14:35:00.000+00:00
Duration: 5 min 39 s
Description: Learn machine learning methods for analyzing fine motor behavior in rodent models of neurodegeneration. This approach combines body part tracking with clustering to classify movements and assess motor deficits reliably.

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This work was supported by the Institute for Mental Health Research, Institute for Social Science Research, Arizona Department of Health Sciences [ADHS14-052688], US Department of Health and Human Services, National Institutes of Health [P30AG019610], Arizona Alzheimer&#39;s Disease Research Center REC Fellows Program,&#160;Arizona Alzheimer&#39;s Consortium, and Nancy Eisenberg Junior Faculty Scholar Award. Figures were created using Biorender.com. We would like to thank Nelson Yamada, Akash Kuppravalli, and Jeanne Kamau for their design assistance.

The authors declare no competing financial interests.

Reaching tasks for rodents are commonly restricted to single pellets and do not include obstructions or self-perturbing features, which are common struggles for patients with neurodegenerative pathology. The method presented here, using both a reimagined pellet-reaching task and machine learning, can detect both coordination and strategy through analysis of pose features and transitions, in addition to individual digit movements. The fine motor task created here detected coordination and strategy via analysis of pose and...

Motor function has been shown to be impaired in people with dementia due to Alzheimer&#39;s disease (AD) or other subtypes (e.g., dementia with Lewy bodies). Grip strength and gait speed can distinguish between cognitively impaired and unimpaired older adults1,2, but cannot necessarily differentiate between different subtypes of dementias (i.e., low specificity)1,2. Thus, there continues to be a need for new diagnostic tools for early dementia, possibly paired with machine learning, for evaluating complex patterns and relationships between behavior and disease state3,4. Animal models are valuable for investigating the progression of neurodegenerative diseases to offer insights into early disease onset, behavioral correlates, and integrating machine learning into analysis pipelines to distinguish between severity and presentation5,6,7.
New behavioral methods in humans have identified tasks that involve fine motor skill along with cognitive processing, like visuospatial ability and executive function8,9,10. For example, a functional upper-extremity task can track patterns of neurodegeneration and can distinguish mild cognitive impairment (MCI) from AD. In this task, humans move raw kidney beans (two at a time) from a central home cup to a target cup using a spoon. The phase of the task in which beans are acquired with the spoon shows stronger associations with cognition (specifically, visuospatial memory) than the phase in which the beans are transported to the target cup11,12. As the spoon enters the cup, it may perturb the beans in the cup, meaning the participant&#39;s movements may make the task more difficult. Further, as the trial progresses and the number of beans in the home cup decreases, the task becomes more difficult (i.e., time to acquire the beans with the spoon increases)13. This task has been shown to correlate with AD-specific pathology (amyloid)9,14 and neurodegeneration (hippocampal atrophy)10,15, suggesting its performance may depend on neural circuitry and behavioral processes that are affected early in the disease process. It is known that movement initiation, planning, execution, and correction of movement are controlled by a diverse network traversing the brain, including the motor cortex, with cell bodies of upper motor neurons located in the cerebral cortex synapsing on lower motor neurons in the spinal cord16. These lower motor neurons directly communicate with muscles to control movement at the neuromuscular junction. Ascending tracts carry sensory information from the periphery to the spinal cord, while descending tracts within the brain communicate directional information regarding how and where to move back to muscles17,18.&#160;Understanding how these pathways are selectively affected by AD pathology is critical to understanding disease progression, particularly since genetic animal models, including the 3xTg-AD mouse and TgF344-AD rat, have been previously used to study how AD genes impact locomotion, coordination, and other motor impairments19,20,21. Typically, these tasks focus on gross motor impairments relating to whole-body movement, but motor deficits in humans are much more variable, which can be difficult to identify in rodents22. New tasks are therefore needed to bridge the gap between human and animal models to capture this variability.
One brain region that is commonly implicated in motor impairments and tremors is the cerebellum23. The cerebellum is a significant regulator of precise and coordinated movements and acts as a processor of sensory information to provide feedback through descending pathways to correct and adapt this voluntary movement24,25. It is important to note that cerebellar atrophy has been recognized in individuals with AD, but, contrary to other regions like the hippocampus, increased amyloid deposition in the cerebellum does not correlate with increased atrophy26. While the progression of AD pathology is different in the cerebellum, there are multi-synaptic bidirectional connections between the cerebellum and hippocampus that are likely disrupted in AD27,28,29. Notably, there are functional connections between the cerebellum and hippocampus, as memory deficits have been documented to occur after cerebellar tumor resection30,31,32,33,34,35,36,37. In rodents, hippocampal place cells are disrupted with a cerebellar perturbation38. These overlapping deficits in memory, sociability, and motor control highlight the importance of uncovering potential early changes in motor pathways that occur before cognitive decline30,31,38,39. With the implementation of fine motor tasks in early AD research, we may begin to reshape the traditional understanding of AD as simply a cognitive disorder and redirect diagnostic efforts to early motor changes that may predict disease progression and may be more greatly distinguishable between cognitively impaired and unimpaired aging individuals.
To understand how fine motor behavior can be indicative of changes in brain structure, a rodent version of the human task described above was created and tested. This rodent task has been named KINEMAT (KINEmatic Motor Ability Task), and it deviates from previous reaching tasks that require the retrieval of a single pellet from a pedestal or staircase and the more simplistic scoring as a result of consistent pellet placement at all stages of the task40,41,42,43,44,45,46,47,48. This fine motor reaching task includes new scoring methods, bowls with various difficulties, and self-perturbing features to mimic the human counterpart, with pellet movement and subsequent participant strategy adjustments central to the design. This task employs the machine learning tools, Social LEAP Estimates Animal Poses (SLEAP) and keypoint-MoSeq49,50, allowing an unbiased and quantitative measurement of fine motor ability and reaching behaviors. While other notable computational models have been developed for skilled reaching tasks in rodents44,45,51, keypoint-MoSeq eliminates noise to allow for precise quantification of movement and grasping poses50. Task performance and motor learning strategy were reconstructed using a high-performance machine vision camera in typical and motor-perturbed rats using Harmaline52. Harmaline hyperactivates the inferior olivary nucleus and disrupts cerebellar function through glutamatergic climbing fibers53,54. In addition, fine motor ability was examined in a subset of TgF344-AD rats. Thus, it was expected that the fine motor task would find measurable impairments in Harmaline-treated and TgF344-AD rats relative to their typical counterparts as a proof-of-concept. This protocol describes constructing the fine motor task, training and testing animals, and quantifying reaching behavior using SLEAP and keypoint-MoSeq.

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a fine motor task to study joint kinematics in a preclinical model of neurodegenerative disease

A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease

Motor dysfunction is a critical, yet often underappreciated, component of neurodegenerative diseases such as Alzheimer's disease (AD) and related dementias. New research has shown that tasks involving fine motor behavior, visuospatial ability, and executive function in humans may have potential in early disease detection. However, little is known about the exact mechanisms by which these tasks predict cognitive and functional decline associated with AD. In the current method, a fine motor task, Kinematic Motor Ability Task (KINEMAT), was created to examine changes in motor ability across disease progression in preclinical models of AD relative to other neurodegenerative diseases and normal aging using free, open-source machine learning software. Here, proof-of-concept was established with the design, testing, and implementation of the fine motor task in a pilot study of Fisher 344 CDF rats with and without Harmaline-induced motor impairments. Additional validation, with an Alzheimer's disease rat model (TgF344-AD), further demonstrated model feasibility and task sensitivity. Rats were trained to reach through a small slot at the front of the chamber to retrieve a sugar pellet from one of three bowls with varying difficulty. Pellet retrieval was scored using one of three classifications: success (successfully grabbing and eating the sugar pellet), drop (grabs but drops the treat upon grasp), or failure (reach in which no pellet is procured, or pellets are knocked out of the bowl when attempting to grasp). Significant differences in motor ability were observed between control and Harmaline-treated rats, demonstrated by reduced total reaches and increased paw pose variability. TgF344-AD rats also showed reduced total reaches, as well as reduced successes across all bowls. In validating this as a fine motor task in rodents, it has future potential to study normal aging and preclinical models of neurodegenerative diseases, including AD, as pathology and symptoms emerge over time.

After rats reached a 90% body weight food restriction, they were habituated to the chamber for 2 days, then underwent shaping procedures for 1-2 weeks. The number of successful, dropped, and failed reaches were scored by a trained observer blind to each condition. Over the course of 9 days (Baseline), rats were tested for fine motor ability on 3 different bowl configurations (Plain, Less, and Plinko). Then, the same rats (n = 5 per group, 1 removed after training bowl shaping due to illness) were injected with Harmaline ...

The current study describes a fine motor behavior test for examining motor deficits in rodent models, including the TgF344-AD rat, using machine learning.

Motor dysfunction is a critical, yet often underappreciated, component of neurodegenerative diseases such as Alzheimer's disease (AD) and related ...

Our research focuses on fine motor skills in models of neurodegeneration. Specifically, we aim to use our new task as a novel biomarker for motor dysfunction. Machine learning algorithms have advanced systems neuroscience to provide an unbiased and automatic way of analyzing clustered animal movement into behavior that typically cannot be deciphered by the human eye.By combining body part tracking and computer clustering methods, we are able to reliably detect, classify, and predict transitions between animal movements in our novel fine motor task. To begin the 10-day shaping phase, first turn on the infrared light, the camera, and the computer. Transfer the cages containing the rats into the testing room and allow them to acclimate for five minutes.Using the training bowl, provide one to five pellets along the extension with the extension placed fully inside the apparatus on the first day. Conduct 10-minute training sessions with each animal. On subsequent days, place the pellets at the end of the bowl closest to the rodent and gradually move them farther away to encourage reaching behavior.Once rodents take pellets from the edge of the extension, even if only with their mouths, introduce pellet presentation with tweezers to promote reaching behavior. Reward the rodent immediately upon touching the tweezers. Then, hold the tweezers slightly farther to prompt the rodent to reach further.For the nine-day testing phase, place the rats in the room to habituate for one hour. Apply dental wax to the platform to fix it in place, then position the first bowl, ensuring the front is flush and centered with the apparatus opening. Name the video file and Spin View with the rat identification number, date of recording, and time of recording.Transfer the rat into the apparatus. Close the lid and position a white cardstock on top to deflect light. Double-check the camera angle to ensure the appropriate scene is captured and adjust the bowl if it is not centered.Pour the specified number of pellets into the bowl and press Start Recording. Stay in the room to monitor the rat, take behavioral notes, and replenish pellets as needed. For the Plain bowl, ensure pellets are visible and accessible over the bowl edge.For the Less bowl, verify that pellets are not stacked and remain loosely scattered. For the Plinko bowl, check that pellets are adequately distributed between obstructions. Return the rat to its home cage and provide the allotted food.Wipe the chamber with a disinfectant between animals. At the end of the day, spray the entire chamber with ethanol for disinfection. During baseline testing, rats showed increased total reaches as the bowl design became more complex.Under Harmaline treatment, total reaches decreased across all bowl types compared to baseline, indicating impaired motor function. During baseline, total reaches increased with bowl difficulty, showing behavioral persistence despite task complexity. Failures increased with the Plinko bowl.Harmaline disrupted typical reach performance. Prior to Harmaline treatment, failures increased with task difficulty under baseline, but remained consistent under Harmaline. Harmaline significantly reduced the learning rate of the task and reaching rate across all bowl types.Analysis of reaches using machine learning revealed unique poses and task strategy between baseline and Harmaline conditions and within each bowl type. Distinct reaching syllables were used by rats within each bowl type and during different bowl conditions. TgF344-AD transgenic rats had reduced total reaches compared to matched wild-type rats.Wild-type rats showed increased total reaches with bowl complexity. While performance type was variable within each group, the TgF344-AD rats had significantly reduced success performance. As bowls became more challenging, the number of failures increased in both genotypes.Behavior can create a lot of noise. In particular, with our task where pellets can be knocked off the platform. We solve this problem with keypoint-MoSeq.Our protocol improves upon past research behavior paradigms that do not allow for self-perturbing features of movement.

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Research

JoVE Journal

Neuroscience

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Learn machine learning methods for analyzing fine motor behavior in rodent models of neurodegeneration. This approach combines body part tracking with clustering to classify movements and assess motor deficits reliably.

A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease

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