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What do across-subject analyses really tell us about neural coding?

Fernando M Ramírez¹, Cambria Revsine¹, Elisha P Merriam¹

¹Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Building 10, Rm 4C118, Bethesda, MD, 20892-1366, USA.

Neuropsychologia

|May 22, 2020

Summary

This summary is machine-generated.

Anatomical normalization is unsuitable for aligning neural representations across subjects in neuroscience. Alternative methods like hyperalignment offer better cross-subject generalization of spatially-structured information.

Keywords:

Cross-validation Hyperalignment Intersubject correlations Leave-one-subject-out MVPA Measurement gain field Mirror symmetry RSA Viewpoint generalization

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Area of Science:

Neuroscience
Computational Neuroscience
Machine Learning in Neuroscience

Background:

Multivariate pattern analysis (MVPA) methods are used to infer neural coding from indirect measures.
A key assumption in cross-subject MVPA is that anatomical normalization aligns spatially-structured neural patterns.
The suitability of anatomical normalization for this purpose remains a critical question.

Purpose of the Study:

To investigate whether anatomical normalization is adequate for aligning neural representations across individual brains.
To identify sources of information revealed by cross-subject analyses when anatomical normalization is insufficient.
To explore alternative methods for testing across-subject generalization of neural information.

Main Methods:

Implemented two-layered feedforward randomly-connected networks with a shared spatial gain-field.
Manipulated image energy profiles and connection densities to simulate signal imbalances and data granularity.
Compared results from anatomical normalization with those from hyperalignment.

Main Results:

Anatomical normalization was found to be unsuitable for aligning neural representations.
Pattern similarity was explained by total-signal imbalances across conditions, not true neural alignment.
Arbitrary analysis choices, like mean-subtraction, can create spurious representational structures.
Hyperalignment emerged as a principled alternative, successfully recovering latent correlation structures in some cases.

Conclusions:

Anatomical normalization is not a reliable method for aligning spatially-structured neural patterns across subjects.
Cross-subject analyses relying on anatomical normalization may reveal artefactual patterns due to signal imbalances and analysis choices.
Hyperalignment offers a more principled approach for assessing across-subject generalization of neural information.
High-resolution individual subject data and informed hyperalignment strategies are crucial for robust inferences in human neuroscience.