AIMC Topic: Magnetic Resonance Imaging

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Dynamic neural network modulation associated with rumination in major depressive disorder: a prospective observational comparative analysis of cognitive behavioral therapy and pharmacotherapy.

Translational psychiatry
Cognitive behavioral therapy (CBT) and pharmacotherapy are primary treatments for major depressive disorder (MDD). However, their differential effects on the neural networks associated with rumination, or repetitive negative thinking, remain poorly u...

Semi-supervised medical image segmentation based on multi-stage iterative training and high-confidence pseudo-labeling.

Biomedical physics & engineering express
Due to the scarcity and high cost of pixel-level annotations for training data, semi-supervised learning has gradually become a key solution. Most existing methods rely on consistency regularization and pseudo-label generation, often adopting multi-b...

Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The endogenous aspect of social influence, reflected in the spontaneous alignment of behaviors within close social relationships, plays a crucial role in understanding human social behavior. In two studies involving 222 human subjects (Study 1:  = 17...

Altered effective connectivity in patients with drug-naïve first-episode, recurrent, and medicated major depressive disorder: A multi-site fMRI study.

Behavioural brain research
BACKGROUND: Major depressive disorder (MDD) has been diagnosed through subjective and inconsistent clinical assessments. Resting-state functional magnetic resonance imaging (rs-fMRI) with connectivity analysis has been valuable for identifying neural...

A deep learning model for diagnosing autism using brain time series.

Neuroscience
The early identification of autism is especially critical as it can significantly enhance the effectiveness of intervention strategies. However, the recognition task remains challenging due to the subtle differences between ASD patients and neurotypi...

Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis.

Clinical and experimental medicine
This study employed machine learning models to quantitatively analyze liver fat content from MRI images for the evaluation of liver fibrosis and disease severity in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). A total o...

Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas.

Cancer imaging : the official publication of the International Cancer Imaging Society
PURPOSE: Accurate preoperative grading of gliomas is critical for therapeutic planning and prognostic evaluation. We developed a noninvasive machine learning model leveraging whole-brain resting-state functional magnetic resonance imaging (rs-fMRI) b...

Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.

BMC medical imaging
BACKGROUND: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a co...

Early prediction of proton therapy dose distributions and DVHs for hepatocellular carcinoma using contour-based CNN models from diagnostic CT and MRI.

Radiation oncology (London, England)
BACKGROUND: Proton therapy is commonly used for treating hepatocellular carcinoma (HCC); however, its feasibility can be challenging to assess in large tumors or those adjacent to critical organs at risk (OARs), which are typically assessed only afte...

TA-SSM net: tri-directional attention and structured state-space model for enhanced MRI-Based diagnosis of Alzheimer's disease and mild cognitive impairment.

BMC medical imaging
Early diagnosis of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is critical for effective prevention and treatment. Computer-aided diagnosis using magnetic resonance imaging (MRI) provides a cost-effective and objectiv...