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Types Of Transformers

Types Of Transformers

Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...

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Related Experiment Video

Updated: Jun 18, 2026

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

Published on: September 22, 2014

4DfCF: 4D fMRI CrossFormer Vision Transformer.

Chensheng Zheng, Shaker El-Sappagh, Tamer Abuhmed

IEEE Journal of Biomedical and Health Informatics

|November 13, 2025

Summary

This summary is machine-generated.

A new 4D functional Magnetic Resonance Imaging (fMRI) CrossFormer model analyzes brain dynamics, improving diagnostic accuracy for neurological conditions like ADHD and Alzheimer's disease. This AI tool enhances precision neuroscience research.

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Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

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Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Analyzing spatiotemporal brain dynamics using functional Magnetic Resonance Imaging (fMRI) is challenging due to complex brain networks and current analytical method limitations.
Existing methods struggle to efficiently process high-dimensional 4D fMRI data for predicting cognitive and clinical outcomes.

Purpose of the Study:

Introduce the 4D functional Magnetic Resonance Imaging (fMRI) CrossFormer (4DfCF), a novel vision transformer architecture for analyzing 4D fMRI data.
Evaluate the 4DfCF model's performance on benchmark datasets for neurological disorders.
Demonstrate the model's potential for advancing precision neuroscience.

Main Methods:

Developed a novel vision transformer architecture (4DfCF) to integrate temporal and spatial dimensions of 4D fMRI data.
Evaluated the 4DfCF model on Attention Deficit Hyperactivity Disorder-200 (ADHD-200), Alzheimer's Disease Neuroimaging Initiative (ADNI), and Autism Brain Imaging Data Exchange (ABIDE) datasets.
Utilized an explainable AI method to identify disease-associated brain regions.

Main Results:

The 4DfCF model consistently outperformed state-of-the-art baseline models, showing significant improvements in accuracy (5-10%), precision (4-8%), recall (6-9%), and F1-score (7-11%).
The 4DfCF-Tiny variant achieved higher efficiency, using 20% less computation and training 30% faster.
Pre-training and fine-tuning experiments demonstrated faster convergence and improved accuracy, with ABIDE pre-trained models showing superior performance.

Conclusions:

The 4D fMRI CrossFormer (4DfCF) offers a powerful and efficient approach for analyzing complex 4D fMRI data.
The model demonstrates significant potential for improving the diagnosis and understanding of neurological and psychiatric disorders.
The findings support the advancement of precision neuroscience through scalable and interpretable AI-driven analysis of brain imaging data.