Group analysis in functional neuroimaging: selecting subjects using similarity measures
Open accessAbstract
Standard group analyses of fMRI data rely on spatial and temporal averaging of individuals. This averaging operation is only sensible when the mean is a good representation of the group. This is not the case if subjects are not homogeneous, and it is therefore a major concern in fMRI studies to assess this group homogeneity. We present a method that provides relevant distances or similarity measures between temporal series of brain functional images belonging to different subjects. The method allows a multivariate comparison between data sets of several subjects in the time or in the space domain. These analyses assess the global intersubject variability before averaging subjects and drawing conclusions across subjects, at the population level. We adapt the RV coefficient to measure meaningful spatial or temporal similarities and use multidimensional scaling to give a visual representation of each subject's position with respect to other subjects in the group. We also provide a measure for detecting subjects that may be outliers. Results show that the method is a powerful tool to detect subjects with specific temporal or spatial patterns, and that, despite the apparent loss of information, restricting the analysis to a homogeneous subgroup of subjects does not reduce the statistical sensitivity of standard group fMRI analyses.
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Small literatures have made attempts to parse fMRI data across individuals using various statistical methods, but none have been sufficiently accepted to fall into wide use.
“Although small literatures have made attempts to parse fMRI data across individuals using various statistical methods (e.g., [11]), none have been sufficiently accepted to fall into wide use.”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [11]
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- Authors
- Kherif, F.
- Affiliations
- MRC Cognition and Brain Sciences Unit (GB)
- Corresponding author
- Kherif, F
- ORCID
- Kherif, F
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2003-12
- Volume
- 20
- Issue
- 4
- Pages
- 2197-2208
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2024545575
- Cited by
- 100
- References
- 52
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 1 · 2020: 4 · 2019: 1 · 2018: 2 · 2017: 1 · 2016: 5 · 2015: 4 · 2014: 4 · 2013: 8 · 2012: 6
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- green
- cea.hal.science
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- group analysis, functional MRI, participant selection, interindividual variability, similarity measures, RV coefficient, multidimensional scaling, outlier detection, spatial and temporal averaging
- MeSH terms
- Algorithms, Data Interpretation, Statistical, Humans, Individuality, Magnetic Resonance Imaging, Models, Neurological, Nervous System, Population, Psychomotor Performance, Time Factors, Reproducibility of Results, Artifacts, Patient Selection
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