Evidence map›Paper›PMID 40196603›Full record

ArticlebioRxiv : the preprint server for biology2025

Systematic bias in surface area asymmetry measurements from automatic cortical parcellations.

Yinuo Liu, Ja Young Choi, Tyler K Perrachione

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Yinuo LiuDepartment of Speech, Language, and Hearing Sciences, Boston University, Boston, Massachusetts, USA.ORCID 0000-0003-2952-3123
Ja Young ChoiDepartment of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA.ORCID 0000-0002-0503-2779
Tyler K PerrachioneDepartment of Speech, Language, and Hearing Sciences, Boston University, Boston, Massachusetts, USA.ORCID 0000-0002-9149-1815

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
Predicting language and literacy growth in children with ASD using statistical learningR56DC020208 · NIDCD · NORTHEASTERN UNIVERSITY · PI QI, ZHENGHAN · 2023 to 2023
$727k
Neural bases of phonological working memory in developmental language disordersR03DC014045 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI PERRACHIONE, TYLER · 2015 to 2017
$491k
Cortical development and neuroanatomical anomalies in developmental dyslexiaR03HD096098 · NICHD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI PERRACHIONE, TYLER · 2018 to 2019
$165k
NICHD NIH HHS R03 HD096098NIDCD NIH HHS R03 DC014045NIDCD NIH HHS R56 DC020208NIMH NIH HHS U54 MH091657
6 · The paper itself

Abstract

Anatomical asymmetry is a hallmark of the human brain and may reflect hemispheric differences in its functional organization. Widely used software like FreeSurfer can automate neuroanatomical measurements and facilitate studies of hemispheric asymmetry. However, patterns of surface area lateralization measured using FreeSurfer are curiously consistent across diverse samples. Here, we demonstrate systematic biases in these measurements obtained from the default processing pipeline. We compared surface area asymmetry measured from reconstructions of original brains vs. the same scans after flipping their left-right orientation. The default pipeline returned implausible asymmetry patterns between the original and flipped brains: Many structures were always left- or right-lateralized. Notably, these biases occur prominently in key speech and language regions. In contrast, manual labeling and curvature-based parcellations of key structures both yielded the expected reversals of left/right lateralization in flipped brains. We determined that these biases result from discrepancies in how regional labels are defined in the left vs. right hemisphere in the default cortical parcellation atlases. These biases are carried into individual parcellations because the FreeSurfer parcellation algorithm prioritizes vertex correspondence to the template atlas relative to individual neuroanatomical variation. We further demonstrate several straightforward, bias-free approaches to measuring surface area asymmetry, including using symmetric registration templates and parcellation atlases, vertex-wise analyses, and within-subject curvature-based parcellations. These results highlight theoretical concerns about using only the default processing stream to make inferences about population-level brain asymmetry and underscore the need for validating bias-free neuroanatomical measurements, particularly when studying regions where structural lateralization may underlie functional lateralization.

Indexed as

Cortical parcellationFreeSurferLateralizationStructural asymmetrySurface area

Identifiers

PMID40196603
PMCPMC11974827

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.