Publications by authors named "Michael R Corigliano"

The use of the common marmoset () for neuroscientific inquiry has grown precipitously over the past two decades. Despite windfalls of grant support from funding initiatives in North America, Europe, and Asia to model human brain diseases in the marmoset, marmoset-specific apparatus are of sparse availability from commercial vendors and thus are often developed and reside within individual laboratories. Through our collective research efforts, we have designed and vetted myriad designs for awake or anesthetized magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), as well as focused ultrasound (FUS), electrophysiology, optical imaging, surgery, and behavior in marmosets across the age-span.

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Article Synopsis
  • The common marmoset has become increasingly popular in neuroscience research over the past 20 years, especially for studying human brain diseases, but marmoset-specific research tools are often limited and must be created within labs.
  • A team has designed and tested various imaging and measurement techniques for studying marmosets, including MRI, PET, CT, and electrophysiology, and has made these designs publicly accessible to help ease the burden on researchers.
  • They provide numerous computer-aided design (CAD) files, software, and resources, including tools for neuroimaging and experimental stimuli, through the Marmoset Brain Connectome website to support further advancements in marmoset neuroscience.
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The reconstruction of two-dimensional (2D) slices to three-dimensional (3D) digital anatomical models requires technical skills and software that are becoming increasingly important to the modern anatomist, but these skills are rarely taught in undergraduate science classrooms. Furthermore, learning opportunities that allow students to simultaneously explore anatomy in both 2D and 3D space are increasingly valuable. This report describes a novel learning activity that trains students to digitally trace a serially imaged neuron from a confocal stack and to model that neuron in 3D space for 3D printing.

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