Human brain lesion-deficit inference remapped
Open accessAbstract
Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant. Positively, we show that novel machine learning techniques employing high-dimensional inference can nonetheless accurately converge on the true locus. We conclude that current inferences about human brain function and deficits based on lesion mapping must be re-evaluated with methodology that adequately captures the high-dimensional structure of lesion data.
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Previous studies were not able to tease apart the impact of damage to Broca's area and surrounding areas because focal damage to Broca's area is rare post-stroke, particularly in ischaemic injuries.
“Previous studies were not able to tease apart the impact of damage to Broca’s area and surrounding areas because focal damage to Broca’s area is rare post-stroke; particularly in the context of ischaemic injuries (Mah et al., 2014; Sperber and Karnath, 2017)”
From Damage to Broca’s area does not contribute to long-term speech production outcome after stroke (2021) · cites this paper as Mah et al., 2014
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“VLSM may also be biased toward the region of maximal overlap in a given sample, affecting the anatomical precision of structure–function claims (Kimberg et al., 2007; Mah et al., 2014).”
Background
- Authors
- Mah, Y.-H., Husain, M., Rees, G., & Nachev, P.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Nachev, Parashkev
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-06-28
- Volume
- 137
- Issue
- 9
- Pages
- 2522-2531
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC4132645
- OpenAlex
- W2045117329
- Cited by
- 336
- References
- 35
- Citations by year
- 2026: 16 · 2025: 21 · 2024: 19 · 2023: 30 · 2022: 37 · 2021: 33 · 2020: 30 · 2019: 35 · 2018: 27 · 2017: 41 · 2016: 18 · 2015: 21 · 2014: 8
- FWCI
- 16.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Brain (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (091593, 100227, ME049685, 098282, 100227/Z/12/Z)
- National Institute for Health and Care Research
- University College London Hospitals NHS Foundation Trust
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Epidemiology › Acute Ischemic Stroke Management
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- focal brain lesions, ischemic stroke, lesion patterns, high-dimensional data, brain localization, diffusion-weighted imaging, automated lesion segmentation, machine learning, vascular architecture
- MeSH terms
- Adult, Aged, Aged, 80 and over, Brain, Brain Mapping, Female, Humans, Male, Middle Aged, Imaging, Three-Dimensional
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