E-Mail
ATLANTA Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture, according to a new study in
Nature Communications led by Georgia State University.
Advanced biomedical technologies such as structural and functional magnetic resonance imaging (MRI and fMRI) or genomic sequencing have produced an enormous volume of data about the human body. By extracting patterns from this information, scientists can glean new insights into health and disease. This is a challenging task, however, given the complexity of the data and the fact that the relationships among types of data are poorly understood.
Caption: Myriam Heiman (left) and Alan Jasanoff have received grants to screen for genes that could help brain cells withstand Parkinson’s disease and to map how gene expression changes in the brain in response to drugs of abuse. Caption: An immunofluorescence image taken in a brain region called the substantia nigra (SN) highlights tyrosine hydroxylase, a protein expressed by dopamine neurons. This type of neuron in the SN is especially vulnerable to neurodegeneration in Parkinson s disease. Credits: Image: Preston Ge/Heiman Lab Caption: Cerebral vasculature in mouse brain. The Jasanoff lab hopes to develop a method for mapping gene expression in the brain with related labeling characteristics.
E-Mail
Researchers from the University of Cambridge, the University of Milan and Google Research have used machine learning techniques to predict how proteins, particularly those implicated in neurological diseases, completely change their shapes in a matter of microseconds.
They found that when amyloid beta, a key protein implicated in Alzheimer s disease, adopts a highly disordered shape, it actually becomes less likely to stick together and form the toxic clusters which lead to the death of brain cells.
The results, reported in the journal
Nature Computational Science, could aid in the future development of treatments for diseases involving disordered proteins, such as Alzheimer s disease and Parkinson s disease.
ClearPoint Neuro, Inc. Announces First Procedure Utilizing ClearPoint 2.0 Software in Europe
IRVINE, Calif., Jan. 14, 2021 (GLOBE NEWSWIRE) ClearPoint Neuro, Inc. (Nasdaq: CLPT), a global therapy-enabling platform company providing navigation and delivery to the brain, today reported the first utilization in Europe of its Version 2.0 software, together with the ClearPoint Neuro Navigation System, at Rigshospitalet in Copenhagen, Denmark. The procedure, which took place last week, also represents the first European site to use the ClearPoint System under live MRI guidance for navigation of a laser catheter in the brain. ClearPoint offers a stereotactic system based on MRI localization with an MRI compatible frame. My clear impression, after my first-time experience, is that the system offers superior accuracy of the stereotactic procedure as compared to our standard frame with CT localization, stated Rune Rasmussen, MD, PhD, Head of Stereotactics at Rigshospitalet. With a sma