Dataset Information

Learning to classify neural activity from a mouse model of Alzheimer's disease amyloidosis versus controls.

ABSTRACT: The mechanisms underlying Alzheimer's disease (AD) onset and progression are not yet elucidated. The extent to which alterations in the activity of individual neurons of an AD model are significant, and the phase at which they can be captured, point to the intensity of the pathology and imply the stage at which it can be detected. Using a machine-learning algorithm, we present a successful cell-by-cell classification of intracellularly recorded neurons from the B6C3 APPswe/PS1dE9 AD model, versus wildtypes controls, at both a late stage and at an early stage, when the plaque pathology and behavioral deficits are absent or rare. These results suggest that the deficits present in neuronal networks of both old and young transgenic animals are large enough to be apparent at the level of individual neurons, and that the pathology could be detected in nearly any given sample, even before pathologic signs.

SUBMITTER: Beker S

PROVIDER: S-EPMC4879657 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Learning to classify neural activity from a mouse model of Alzheimer's disease amyloidosis versus controls.

Beker Shlomit S Kellner Vered V Chechik Gal G Stern Edward A EA

Alzheimer's & dementia (Amsterdam, Netherlands) 20160203

The mechanisms underlying Alzheimer's disease (AD) onset and progression are not yet elucidated. The extent to which alterations in the activity of individual neurons of an AD model are significant, and the phase at which they can be captured, point to the intensity of the pathology and imply the stage at which it can be detected. Using a machine-learning algorithm, we present a successful cell-by-cell classification of intracellularly recorded neurons from the B6C3 APPswe/PS1dE9 AD model, versu ...[more]

PMID: 27239535

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Project description:Reduced cerebral blood flow impairs cognitive function and ultimately causes irreparable damage to brain tissue. The gliovascular unit, composed of neural and vascular cells, assures sufficient blood supply to active brain regions. Astrocytes, vascular smooth muscle cells, and pericytes are important players within the gliovascular unit modulating vessel diameters. While the importance of the gliovascular unit and the signals involved in regulating local blood flow to match neuronal activity is now well recognized, surprisingly little is known about this interface in disease. Alzheimer's disease is associated with reduced cerebral blood flow. Here, we studied how the gliovascular unit is affected in a mouse model of Alzheimer's disease, using a combination of ex vivo and in vivo imaging approaches. We specifically labelled vascular amyloid in living mice using the dye methoxy-XO4. We elicited vessel responses ex vivo using either pharmacological stimuli or cell-specific calcium uncaging in vascular smooth muscle cells or astrocytes. Multi-photon in vivo imaging through a cranial window allowed us to complement our ex vivo data in the presence of blood flow after label-free optical activation of vascular smooth muscle cells in the intact brain. We found that vascular amyloid deposits separated astrocyte end-feet from the endothelial vessel wall. High-resolution 3D images demonstrated that vascular amyloid developed in ring-like structures around the vessel circumference, essentially forming a rigid cast. Where vascular amyloid was present, stimulation of astrocytes or vascular smooth muscle cells via ex vivo Ca(2+) uncaging or in vivo optical activation produced only poor vascular responses. Strikingly, vessel segments that were unaffected by vascular amyloid responded to the same extent as vessels from age-matched control animals. We conclude that while astrocytes can still release vasoactive substances, vascular amyloid deposits render blood vessels rigid and reduce the dynamic range of affected vessel segments. These results demonstrate a mechanism that could account in part for the reduction in cerebral blood flow in patients with Alzheimer's disease.media-1vid110.1093/brain/awv327_video_abstractawv327_video_abstract.

Dataset Information

Learning to classify neural activity from a mouse model of Alzheimer's disease amyloidosis versus controls.

Publications

Learning to classify neural activity from a mouse model of Alzheimer's disease amyloidosis versus controls.

Similar Datasets

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