A deep learning-based toolbox for Automated Limb Motion Analysis (ALMA) in murine models of neurological disorders

Aljović, Almir; Zhao, Shuqing; Chahin, Maryam; Rosa, Clara de la; Steenbergen, Valérie Van; Kerschensteiner, Martin; Bareyre, Florence M.

doi:10.1038/s42003-022-03077-6

Cited by 21 publications

(18 citation statements)

References 63 publications

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“…To test whether delivering FGF22 can also be therapeutically applied after SCI, we sought to assess recovery of motor function that could be achieved when global FGF22 gene therapy was delivered either immediately after the injury (acute treatment), 1 day after the injury (early treatment: 1 dpi), or 5‐day postinjury (late treatment: 5 dpi). For all three application protocols, we evaluated functional recovery using the ladder rung test that evaluates fine paw placement (Liebetanz & Merkler, 2006 ; Loy et al , 2018 ; Aljovic et al , 2022 ; Fig 4A–E ). We tested two ladder rung paradigms: In the first, the rungs are regularly spaced, to assess paw placements and general locomotion.…”

Section: Resultsmentioning

confidence: 99%

“…As our gene therapy approach is efficient to increase new circuit wiring following SCI, we then determined when such an approach has to be initiated to trigger functional recovery. We used the ladder rung test (Metz & Whishaw, 2009 ; Jacobi et al , 2015 ; Loy et al , 2018 ; Aljovic et al , 2022 ) which, among other parameters, provides a readout for successful CST rewiring since constant step‐length adaptations requires supraspinal input (Liebetanz & Merkler, 2006 ). We demonstrate that when delivered at the time of injury or within 1 day later (1 dpi), gene therapy with FGF22 triggers a significant improvement in CST‐dependent stepping abilities in the irregular ladder rung test.…”

Section: Discussionmentioning

confidence: 99%

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Synaptogenic gene therapy with FGF22 improves circuit plasticity and functional recovery following spinal cord injury

et al. 2023

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Functional recovery following incomplete spinal cord injury (SCI) depends on the rewiring of motor circuits during which supraspinal connections form new contacts onto spinal relay neurons. We have recently identified a critical role of the presynaptic organizer FGF22 for the formation of new synapses in the remodeling spinal cord. Here, we now explore whether and how targeted overexpression of FGF22 can be used to mitigate the severe functional consequences of SCI. By targeting FGF22 expression to either long propriospinal neurons, excitatory interneurons, or a broader population of interneurons, we establish that FGF22 can enhance neuronal rewiring both in a circuit‐specific and comprehensive way. We can further demonstrate that the latter approach can restore functional recovery when applied either on the day of the lesion or within 24 h. Our study thus establishes viral gene transfer of FGF22 as a new synaptogenic treatment for SCI and defines a critical therapeutic window for its application.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Synaptogenic gene therapy with FGF22 improves circuit plasticity and functional recovery following spinal cord injury

et al. 2023

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“…Vid (2-photon calcium), Seq Yes Event detection ScLimibic [66] Neuroimaging Img (MRI) External Segm. ALMA [67] Behavioral neuroscience Vid External Pose est., Class.…”

Section: Imgmentioning

confidence: 99%

An overview of open source Deep Learning-based libraries for Neuroscience

Tshimanga¹,

Atzori²,

Pup³

et al. 2023

Preprint

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In recent years, deep learning revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis. Due to the fast growth of the domain, it could be a complicated and extremely time-consuming task for worldwide researchers to have a clear perspective of the most recent and advanced software libraries. This work contributes to clarify the current situation in the domain, outlining the most useful libraries that implement and facilitate deep learning application to neuroscience, allowing scientists to identify the most suitable options for their research or clinical projects. This paper summarizes the main developments in Deep Learning and their relevance to Neuroscience; it then reviews neuroinformatic toolboxes and libraries, collected from the literature and from specific hubs of software projects oriented to neuroscience research. The selected tools are presented in tables detailing key features grouped by domain of application (e.g. data type, neuroscience area, task), model engineering (e.g. programming language, model customization) and technological aspect (e.g. interface, code source). The results show that, among a high number of available software tools, several libraries are standing out in terms of functionalities for neuroscience applications. The aggregation and discussion of this information can help the neuroscience community to devolop their research projects more efficiently and quickly, both by means of readily available tools, and by knowing which modules may be improved, connected or added.

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“…17,18 Machine learning has opened compelling avenues for studying complex motor behaviors in rodents without the need for marker-based motion tracking. [19][20][21][22][23][24][25][26] These tools have helped unravel neural circuits responsible for coordinated hand function in physiological states in mice. 27,28 More recently, these tools have been successfully adopted for the analysis of gait in mice following cortical photothrombosis.…”

Section: Introductionmentioning

confidence: 99%

Refined movement analysis in the Staircase test reveals differential motor deficits in mouse models of stroke

Skrobot,

Sa,

Walter

et al. 2023

Preprint

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Accurate assessment of post-stroke deficits is vital in translational research. Recent advances in machine learning provide unprecedented precision in quantifying rodent motor behavior post-stroke. However, the extent to which these tools can detect lesion-specific upper extremity deficits remains unclear. Using proximal middle cerebral artery occlusion (MCAO) and cortical photothrombosis (PT), we assessed post-stroke impairments in mice through the Staircase test. Lesion locations were identified using 7T-MRI. Machine learning was applied to reconstruct kinematic trajectories using MouseReach, a data-processing toolbox. This yielded 30 refined outcome parameters effectively capturing motor deficits. Lesion reconstructions located ischemic centers in the striatum (MCAO) and sensorimotor cortex (PT). Pellet retrieval was altered in both cases but did not correlate with stroke volume or ischemia extent. Instead, cortical ischemia was characterized by increased hand slips and modified reaching success. Striatal ischemia led to progressively prolonged reach durations, mirroring delayed symptom onset in basal ganglia strokes. In summary, refined machine learning-based movement analysis revealed specific deficits in mice after cortical and striatal ischemia. These findings emphasize the importance of thorough behavioral profiling in preclinical stroke research to increase translational validity of behavioral assessments.

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A deep learning-based toolbox for Automated Limb Motion Analysis (ALMA) in murine models of neurological disorders

Cited by 21 publications

References 63 publications

Synaptogenic gene therapy with FGF22 improves circuit plasticity and functional recovery following spinal cord injury

Synaptogenic gene therapy with FGF22 improves circuit plasticity and functional recovery following spinal cord injury

An overview of open source Deep Learning-based libraries for Neuroscience

Refined movement analysis in the Staircase test reveals differential motor deficits in mouse models of stroke

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