Topic: K-means clustering

Dendritic spines of pyramidal neurons are thin protrusions of the neuron membrane and represent the main postsynaptic sites in the cerebral cortex. Spines can have different morphologies: investigating their shape gives indications about the functional state of synapses during brain development. Quantifying dendritic spine shape changes will boost the comprehension of several neuropathies, such as Alzheimer and autism spectrum. Detection of dendritic spines is challenged by optical limitations. We propose a fully automated pipeline to segment and cluster spines to classify them according to th

We aimed to characterize cognitive profiles and brain morphology correlates of Prodromal and Parkinson's disease (PD) patients from the Parkinson's Progression Markers Initiative program, given the value of detecting Mild Cognitive Impairment (MCI) as a risk factor for dementia in the Prodromal stage. K-means clustering analysis was performed to identify distinct cognitive profiles based on the cognitive domain scores of the Montreal Cognitive Assessment, while volumetric quantification of brain regions was conducted using the AssemblyNet pipeline on T13D MRI brain images. Distinct cognitive p

Characterizing brain oscillatory activity at the single-subject level is a key concern in personalized medicine. A recent publication has presented a normative map of the brain’s natural or typical frequencies of oscillation that could be used for detecting potential deviations in healthcare. However, this map represents brain activity in a voxel-by-voxel basis from high-density recordings of a large sample of healthy volunteers, compared to the low-density recordings usually employed on patients. Thus, accounting for less data to construct the single-subject map, a significant portion of nois