The current study describes a fine motor behavior test for examining motor deficits in rodent models, including the TgF344-AD rat, using machine learning.
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Method Article
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 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.
Motor function has been shown to be impaired in people with dementia due to Alzheimer'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....
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All methods described here have been approved by the Institutional Animal Care and Use Committee (IACUC) of Arizona State University.
1. Design and construction of the chamber
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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 .......
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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.......
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The authors declare no competing financial interests.
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's Disease Research Center REC Fellows Program, Arizona Alzheimer'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.
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| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| 3D printing material | Amazon (Overture) | B07PGY2JP1 | PLA Filament 1.75 mm PLA 3D Printer Filament, Dimensional Accuracy +/- 0.03 mm |
| Camera | Edmond Optics | BFS-U3-13Y3M-C | 1.3 MP, Mono, 170 FPS, ON Semi PYTHON 1300 x2 |
| Camera cables | Edmond Optics | ACC-01-2300 | USB 3.1, 3 m, Type-A to Micro-B (Locking) Cable |
| Camera lens | Thor labs | MVL5WA | 4.5 mm EFL, F/1.4, 1/2" Format Machine Vision Lens |
| Camera to tripod connection | Edmond Optics | 88-210 | Connection for camera to tripod |
| Cleaning wipes | Amazon (Sani-Cloth) | B00KMZ7KMO | AF3 Surface Disinfectant Cleaner Wipe Canister Mild Scent 160 Ct P13872 |
| Computer | Dell | Precision | Intel Core i7-13700 @ 2.1 GHz, NVIDIA T1000 8 GB, RAM 16 GB |
| IR light | Amazon (Serlium) | B0BJFDBT44 | Camera IR Light 48 LED IR Illuminators Lights Waterproof Infrared Night Visionss Light for Security CCTV Camera |
| Panel glue | Amazon (Sdintar) | B0B1DLRPNZ | Glass glue |
| Plexiglass | Amazon (TOOLINHAND US) | B0BRJ1L8TR | 12 × 12" Clear Cast Acrylic/Plexiglass Sheets - Transparent 1/8” Thick (3 mm) |
| Python | Python Software Foundation | Python 3.7 | |
| Spinview | Teledyne Technologies | Version 3.1.0.79 | |
| Sugar pellets (45 mg) | Bioserv | F0023 | 45 mg, Unflavored 50,000/Box; Ingredients: Sucrose, Dextrose, Magnesium Stearate, Calcium Silicate, Mineral Oil |
| Tripod | Amazon (YOUYI) | B086PYJF9D | Amazon Flexible Desk Webcam Tripod |
| Tweezers | Fine Science Tools | 11064-07 | Iris Forceps |
| Wax | Amazon (Orthomechanics) | B00FKDC0UU | Genuine Orthowax |
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