AIMC Topic: Cerebellum

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Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.

Parkinsonism & related disorders Mar 7, 2021
INTRODUCTION: Predictive biomarkers of Parkinson's Disease progression are needed to expedite neuroprotective treatment development and facilitate prognoses for patients. This work uses measures derived from resting-state functional magnetic resonanc...
Male Middle Aged Disease Progression Severity of Illness Index Female Reproducibility of Results Aged Cerebellum Cerebral Cortex Nerve Net Default Mode Network Functional Neuroimaging Follow-Up Studies Magnetic Resonance Imaging Parkinson Disease Humans Machine Learning Biomarkers Prognosis
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Fast and precise single-cell data analysis using a hierarchical autoencoder.

Nature communications Feb 15, 2021
A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical ...
Pancreas Zygote Cell Separation Mouse Embryonic Stem Cells Retina Cerebellum Embryo, Mammalian Visual Cortex Single-Cell Analysis Mice Sequence Analysis, RNA Liver Unsupervised Machine Learning Bayes Theorem Benchmarking Humans Neural Networks, Computer Animals Lung
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Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways.

PLoS computational biology Feb 10, 2021
The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented thro...
Animals Signal Transduction Neuronal Plasticity Models, Neurological Humans Neural Networks, Computer Computer Simulation Interneurons Purkinje Cells Excitatory Postsynaptic Potentials Cerebellar Cortex Synapses Neural Pathways Normal Distribution Synaptic Transmission Action Potentials Neurons Cerebellum
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A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing.

Journal of the Royal Society, Interface Jan 27, 2021
The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing ta...
Brain Learning Robotics Cerebellum Humans
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Human-scale Brain Simulation via Supercomputer: A Case Study on the Cerebellum.

Neuroscience Jan 20, 2021
Performance of supercomputers has been steadily and exponentially increasing for the past 20 years, and is expected to increase further. This unprecedented computational power enables us to build and simulate large-scale neural network models compose...
Models, Neurological Neurons Cerebellum Humans Neural Networks, Computer Brain Computer Simulation
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Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning.

Scientific reports Oct 29, 2020
Eye movements are disrupted in many neurodegenerative diseases and are frequent and early features in conditions affecting the cerebellum. Characterizing eye movements is important for diagnosis and may be useful for tracking disease progression and ...
Cell Phone Pursuit, Smooth Eye Movements Child, Preschool Child Cerebellum Adult Male Adolescent Humans Machine Learning Female Young Adult
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Population coding in the cerebellum: a machine learning perspective.

Journal of neurophysiology Oct 28, 2020
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron i...
Humans Machine Learning Learning Animals Eye Movements Conditioning, Classical Purkinje Cells Cerebellar Nuclei Action Potentials Cerebellum Motor Activity
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Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning.

Analytical chemistry Jun 25, 2020
The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, c...
Single-Cell Analysis Rats Mass Spectrometry Principal Component Analysis Machine Learning Animals ROC Curve Area Under Curve Cerebellum Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Hippocampus
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Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization.

NeuroImage May 11, 2020
The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a w...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Magnetic Resonance Imaging Cerebellum Neuroimaging
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A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET-CT scans.

Medical physics Jan 6, 2020
PURPOSE: The purpose of this work was to assess the potential of deep convolutional neural networks in automated measurement of cerebellum tracer uptake in F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scans.
Fluorodeoxyglucose F18 Positron Emission Tomography Computed Tomography Biological Transport Cerebellum Automation Humans Neural Networks, Computer Imaging, Three-Dimensional
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