Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results
Anton Orlichenko1, Gang Qu1, Kuan-Jui Su2
1Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA.
Arxiv
|August 14, 2023
Summary
Researchers can unintentionally inflate brain imaging results by treating data from the same individual as independent. This method, using functional connectivity, can boost classification accuracy significantly, impacting future research findings.
Area of Science:
- Neuroimaging
- Cognitive Neuroscience
- Machine Learning in Neuroscience
Background:
- Functional magnetic resonance imaging (fMRI) is crucial for in vivo cognitive studies.
- Functional connectivity (FC) and related metrics are increasingly used to predict phenotypes.
- Replicability issues in fMRI studies are a growing concern.
Approach:
- Demonstrate how treating longitudinal/contemporaneous scans of the same subject as independent inflates classification accuracy.
- Utilize the UK Biobank dataset to show identifiability can explain variance with fewer subjects.
- Replicate findings across four diverse datasets: UK Biobank, PNC, BSNIP, and a Fibromyalgia dataset.
Key Points:
- Unintentional accuracy inflation from 61% to 86% observed by not accounting for subject identifiability.
- Achieved similar predictive power with 50 subjects using identifiability as with 10,000 subjects without.
- Accuracy improvements ranged from 7% to 25% across datasets.
- Dynamic functional connectivity (dFC) allows this inflation even with single scans per subject.
Conclusions:
- Minor pipeline anomalies, like overlooking subject identifiability, can lead to inflated results.
- Features derived from inflated results may mislead future research.
- Awareness and correction of such biases are critical for robust neuroimaging research.
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