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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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Updated: Apr 26, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Deficient approaches to human neuroimaging.

Johannes Stelzer¹, Gabriele Lohmann², Karsten Mueller³

¹Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre Hvidovre, Denmark.

Frontiers in Human Neuroscience

|July 30, 2014

Summary

Functional magnetic resonance imaging (fMRI) analysis methods are increasingly scrutinized for accuracy. Critiques highlight significant false positive and negative findings in brain mapping, necessitating methodological reassessment.

Keywords:

brain mapping cognitive neuroscience critical neuroscience fMRI functional localization

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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Area of Science:

Cognitive Neuroscience
Neuroimaging
Neuroscience

Background:

Functional magnetic resonance imaging (fMRI) is a cornerstone of cognitive neuroscience research.
fMRI study publication rates have surged, with recent years seeing more publications than the previous 17 combined.
Current research predominantly focuses on localizing functional brain activity via the blood-oxygenation-level dependent (BOLD) signal.

Purpose of the Study:

To critically reassess common analysis frameworks and methodologies in fMRI.
To provide an overview of conceptual and practical deficiencies in widely used brain-mapping approaches.
To highlight issues in statistical parametric mapping (SPM) and their implications.

Main Methods:

Review and critique of existing fMRI analysis frameworks.
Use of imaging data and simulations to exemplify methodological shortcomings.
Focus on statistical parametric mapping (SPM) deficiencies.

Main Results:

Identified significant conceptual and practical deficiencies in common fMRI brain-mapping approaches.
Demonstrated inherent pitfalls and shortcomings in statistical parametric mapping methodologies.
Highlighted recent reports of excessively high false positive and false negative rates in fMRI.

Conclusions:

Widely used fMRI analysis methods, particularly SPM, suffer from critical deficiencies.
These deficiencies lead to a high prevalence of inaccurate findings (both false positives and negatives).
Methodological reassessment and potential solutions are crucial for the integrity of cognitive neuroscience research using fMRI.