AIMC Topic: Caudate Nucleus
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Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI.
Progress in neuro-psychopharmacology & biological psychiatry
May 11, 2024
It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted r...
External evaluation of a deep learning-based approach for automated brain volumetry in patients with huntington's disease.
Scientific reports
Apr 22, 2024
A crucial step in the clinical adaptation of an AI-based tool is an external, independent validation. The aim of this study was to investigate brain atrophy in patients with confirmed, progressed Huntington's disease using a certified software for au...
An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.
Human brain mapping
Jun 19, 2023
Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution ...
The neuromorphological caudate-putaminal clustering of neostriate interneurons: Kohonen self-organizing maps and supervised artificial neural networks with multivariate analysis.
Journal of theoretical biology
Nov 21, 2017
AIMS: The objective of this study is to investigate the possibility of the neuromorphotopological clustering of neostriate interneurons (NSIN) and their consequent classification into caudate (CIN) and putaminal neuron type (PIN), according to the nu...
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