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Related Concept Videos

Olfaction01:25

Olfaction

49.7K
The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
49.7K

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An Objective and Reproducible Test of Olfactory Learning and Discrimination in Mice
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Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach.

Matthew C Murphy1, Alexander J Poplawsky2, Alberto L Vazquez2

  • 1Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.

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|May 29, 2016
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Summary

This study developed a data-driven model to enhance the spatial accuracy of functional MRI (fMRI) activation maps in rat olfactory bulbs. The new model improves fMRI spatial accuracy, particularly for BOLD images.

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Area of Science:

  • Neuroimaging
  • Systems Neuroscience
  • Animal Models

Background:

  • Functional MRI (fMRI) is crucial for noninvasive neural activity mapping.
  • fMRI's hemodynamic measurements limit perfect reflection of neural activity.
  • Spatial accuracy of fMRI maps can be improved.

Purpose of the Study:

  • To design a data-driven model for enhancing spatial accuracy of fMRI maps.
  • To improve fMRI spatial accuracy specifically in the rat olfactory bulb.
  • To generate models for both cerebral blood volume weighted (CBVw) and blood oxygen level dependent (BOLD) fMRI data.

Main Methods:

  • Developed a data-driven model tailored for the rat olfactory bulb circuit.
  • Trained the model using well-characterized activity patterns.
  • Generated models for CBVw and BOLD fMRI data.

Main Results:

  • Learned models significantly improved spatial accuracy of fMRI activation maps compared to conventional general linear models.
  • Improvements were most notable for BOLD images and deep bulb layer activity.
  • Increased agreement between CBVw and BOLD activation maps using the learned models.

Conclusions:

  • The data-driven models enhance the spatial accuracy and reliability of fMRI activation maps.
  • These models have immediate applications for olfactory bulb studies.
  • Demonstrates potential for flexible, data-driven models to improve fMRI data quality across neuroscience research.