AIMC Topic: Brain
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IConDiffNet: an unsupervised inverse-consistent diffeomorphic network for medical image registration.
Physics in medicine and biology
Feb 20, 2025
Deformable image registration (DIR) is critical in many medical imaging applications. Diffeomorphic transformations, which are smooth invertible mappings with smooth inverses preserve topological properties and are an anatomically plausible means of ...
Integrative diagnosis of psychiatric conditions using ChatGPT and fMRI data.
BMC psychiatry
Feb 19, 2025
BACKGROUND: Traditional diagnostic methods for psychiatric disorders often rely on subjective assessments, leading to inconsistent diagnoses. Integrating advanced natural language processing (NLP) techniques with neuroimaging data may improve diagnos...
Ensemble fuzzy deep learning for brain tumor detection.
Scientific reports
Feb 19, 2025
This research presents a novel ensemble fuzzy deep learning approach for brain Magnetic Resonance Imaging (MRI) analysis, aiming to improve the segmentation of brain tissues and abnormalities. The method integrates multiple components, including dive...
Characterizing Brain-Cardiovascular Aging Using Multiorgan Imaging and Machine Learning.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Feb 19, 2025
The structure and function of the brain and cardiovascular system change over the lifespan. In this study, we aim to establish the extent to which age-related changes in these two vital organs are linked. Utilizing normative models and data from the ...
Compression-enabled interpretability of voxelwise encoding models.
PLoS computational biology
Feb 19, 2025
Voxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models ...
IRMA: Machine learning-based harmonization of F-FDG PET brain scans in multi-center studies.
European journal of nuclear medicine and molecular imaging
Feb 18, 2025
PURPOSE: Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.
Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.
European journal of nuclear medicine and molecular imaging
Feb 18, 2025
PURPOSE: Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ c...
Clinical efficacy of NIBS in enhancing neuroplasticity for stroke recovery.
Journal of neuroscience methods
Feb 18, 2025
BACKGROUND: For stroke patients, a therapeutic approach named Non-invasive brain stimulation (NIBS) was applied and it has gained attention. This NIBS approach enhances the neuroplasticity and facilitates in functional Stroke Rehabilitation (SR) thro...
Interpretation of basal nuclei in brain dopamine transporter scans using a deep convolutional neural network.
Nuclear medicine communications
Feb 18, 2025
OBJECTIVE: Functional imaging using the dopamine transporter (DAT) as a biomarker has proven effective in assessing dopaminergic neuron degeneration in the striatum. In assessing the neuron degeneration, visual and semiquantitative methods are used t...
Aged
Male
Middle Aged
Deep Learning
Brain
Retrospective Studies
Female
Tomography, Emission-Computed, Single-Photon
Dopamine Plasma Membrane Transport Proteins
Convolutional Neural Networks
Image Processing, Computer-Assisted
Parkinson Disease
Image Interpretation, Computer-Assisted
Humans
Neural Networks, Computer
A simple clustering approach to map the human brain's cortical semantic network organization during task.
NeuroImage
Feb 18, 2025
Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. Howev...
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