Co-registration of imaging modalities (MRI, CT and PET) to perform frameless stereotaxic robotic injections in the common marmoset.

2021 
The common marmoset has emerged as a popular model in neuroscience research, in part due to its reproductive efficiency, genetic and neuroanatomical similarities to humans and the successful generation of transgenic lines. Stereotaxic procedures in marmosets are guided by 2D stereotaxic atlases, which are constructed with a limited number of animals and fail to account for inter-individual variability in skull and brain size. Here, we developed a frameless imaging-guided stereotaxic system that improves upon traditional approaches by using subject-specific registration of computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) data to identify a surgical target, namely the putamen, in two marmosets. The skull surface was laser-scanned with to create a point cloud that was registered to the 3D reconstruction of the skull from CT. Reconstruction of the skull, as well as of the brain from MR images, was crucial for surgical planning. Localisation and injection into the putamen was done using a 6-axis robotic arm controlled by a surgical navigation software (BrainsightTM). Integration of subject-specific registration and frameless stereotaxic navigation allowed target localisation specific to each animal. Injection of alpha-synuclein fibrils into the putamen triggered progressive neurodegeneration of the nigro-striatal system, a key feature of Parkinson's disease. Four months post-surgery, a PET scan found evidence of nigro-striatal denervation, supporting accurate targeting of the putamen during co-registration and subsequent surgery. Our results suggest that this approach, coupled with frameless stereotaxic neuronavigation, is accurate in localising surgical targets and can be used to assess endpoints for longitudinal studies.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    70
    References
    0
    Citations
    NaN
    KQI
    []