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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Sep 11, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

The future of data analysis is now: Integrating generative AI in neuroimaging methods development.

Elizabeth DuPre¹, Russell Alan Poldrack¹

¹Department of Psychology, Stanford University, Stanford, CA, United States.

Imaging Neuroscience (Cambridge, Mass.)

|August 13, 2025

Summary

Emerging artificial intelligence (AI) tools will transform computational fMRI analysis by automating procedures and aiding neuroimaging method development. AI is expected to enhance, not replace, open science practices in computational neuroscience.

Keywords:

artificial intelligence code assistants computational science neuroimaging methods open science

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Area of Science:

Computational neuroscience
Neuroimaging analysis
Artificial intelligence in research

Background:

The field of data science has long advocated for the automation of statistical procedures.
Generative artificial intelligence (AI) presents novel opportunities to enhance current practices in computational neuroscience.
AI tools are expected to impact both the development of new neuroimaging methods and daily research tasks.

Purpose of the Study:

To highlight the impact of emerging AI tools on researchers performing computational fMRI analyses.
To discuss how generative AI can advance neuroimaging methods development and daily research practices.
To argue for viewing AI as a catalyst for computational neuroscience and reinforcing open science initiatives.

Main Methods:

Perspective piece synthesizing current trends and future implications of AI in fMRI analysis.
Discussion of AI's role in neuroimaging method development (e.g., image quality control) and analysis code generation.
Argument for integrating AI within existing research frameworks, emphasizing open science principles.

Main Results:

Generative AI is poised to significantly impact computational fMRI analysis, from method development to daily coding.
AI is expected to reinforce, rather than replace, the importance of open science initiatives in the field.
The integration of AI necessitates the development of clearer metrics for comparing neuroimaging results.

Conclusions:

Generative AI should be viewed as a catalyst to advance computational neuroscience and its integration into the broader research ecosystem.
AI tools will likely enhance existing open science practices, promoting transparency and reproducibility.
Establishing standardized metrics for evaluating neuroimaging results is crucial for meaningful comparisons, regardless of whether AI is involved in their generation.