Confounds in Functional Magnetic Resonance Imaging—Part III: Auxiliary Data

In the first part of this series, we reviewed the pathways that bring about confounding nuisance signals in fMRI. Then, in the second part, we considered how human factors can modify these pathways. In this, the third and final part, we review auxiliary data that may allow researchers to identify and perhaps correct for nuisance signals and their modifiers. We consider three broad categories of data: (1) measures that can be collected synchronously with the fMRI, (2) other MRI scans that either capture trait information or attempt to define the state that exists during an fMRI scan in the same session, and (3) non-MRI measures that can be collected before or after the scan and that also capture trait or relevant state factors influencing the fMRI. We conclude with some recommendations on ways to improve fMRI experiments going forward.

Authors

Institutions

Publication Details

Journal
Journal of Cognitive Neuroscience
Published
2026-11-06
DOI
https://doi.org/10.1162/jocn.a.2736
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Confounds in Functional Magnetic Resonance Imaging—Part III: Auxiliary Data

Ben A. Inglis, Mark D'Esposito
2 links
Journal of Cognitive Neuroscience
Advanced MRI Techniques and Applications
article

Confounds in Functional Magnetic Resonance Imaging—Part III: Auxiliary Data

Ben A. Inglis, Mark D'Esposito
article en

Abstract

In the first part of this series, we reviewed the pathways that bring about confounding nuisance signals in fMRI. Then, in the second part, we considered how human factors can modify these pathways. In this, the third and final part, we review auxiliary data that may allow researchers to identify and perhaps correct for nuisance signals and their modifiers. We consider three broad categories of data: (1) measures that can be collected synchronously with the fMRI, (2) other MRI scans that either capture trait information or attempt to define the state that exists during an fMRI scan in the same session, and (3) non-MRI measures that can be collected before or after the scan and that also capture trait or relevant state factors influencing the fMRI. We conclude with some recommendations on ways to improve fMRI experiments going forward.

Journal of Cognitive Neuroscience
University of California System (US)
Good health and well-being
Openalex Percentile: Top 17%
Advanced MRI Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon