‘Overdue’ debate unfurls over neuroimaging method After a January paper questioned the validity of an approach called lesion network mapping, its users are pressure testing their results.

A wave of new papers discuss the merits and limitations of lesion network mapping. #neuroskyence

By @avaskham.bsky.social

www.thetransmitter.org/brain-imagin...

1/9
New preprint by Laurin Mordhorst: 3D histology validates 2D histology for axon radius distributions and conduction velocities.
We asked: if individual axons vary along their length, can classic 2D histology still reflect white-matter organization?
Link: www.biorxiv.org/content/10.6...

Opportunities and pitfalls of data contextualization in neuroimaging - Nature Reviews Neuroscience Despite rapid exploitation of the opportunities that contextualization of brain maps affords, potential limitations have received little attention. In this Roadmap, Royer et al. provide practical guid...

Correlating brain maps across datasets is everywhere in neuroimaging. Here we ask: when you contextualize a brain map against genes, metabolism, or connectivity... What can you really conclude? How can we do better? We explore these questions here: tinyurl.com/2dudkevc

Null models for lesion network mapping - Nature Neuroscience Van den Heuvel et al. show that lesion-network mapping (LNM)-derived circuits converge across disorders and argue that this convergence reflects a fundamental methodological limitation of LNM. We eval...

The LNM debate keeps developing.

www.nature.com/articles/s41...

@andrewzalesky.bsky.social @natneuro.nature.com #neuroskyence

Organization of neuropeptide systems in the human brain | doi.org/10.1038/s415...

Neuropeptides are functionally diverse signaling molecules in the brain and body.

@cebric.bsky.social curates an atlas of neuropeptide receptors and relates it to brain function @natneuro.nature.com 🧩 🧠 ⤵️

🚨 New preprint alert! 🚨 Transdiagnostic latent factor models of psychopathology are widely assumed to improve brain-behaviour associations. So we decided to test this directly and found that they don't. A short 🧵

Link: www.biorxiv.org/content/10.6...

1/10

🧠 Resting-state fMRI is often treated as the gold standard for studying the brain’s intrinsic organization.

But is it actually the best way to estimate functional architecture?

We tested this directly.

🧵1/8

I couldn't find a tool to plot different #neuroimaging data in one consistent style, so I made one! Meet yabplot (yet another brain plot) - a #Python package for (sub)cortex & tracts.🧠
- Simple API
- Built-in atlases
- Custom atlas support
🔗 github.com/teanijarv/ya... (drop a ⭐️!)

Spatial Null Models and Transforms for Brain Map Comparison Implements spatial null models and coordinate-space transformations for statistical comparison of brain maps, following the framework described in Markello et al. (2022)…

neuromapr 0.2.1 has been accepted and published on CRAN!

Very excited to get this out to users in the simplest way possible, and hope the #rstats #neuroimaging community finds it useful!

lcbc-uio.github.io/neuromapr/

A recent @natneuro.nature.com paper analyzed lesion network mapping and raised concerns about the validity of the method.
See below 👇 for our response.

www.biorxiv.org/content/10.6...

A recent paper in @natureportfolio.nature.com raised concerns about the lesion network mapping method. Our team of 16 coauthors analyzed >1000 lesions and 34 symptoms and found that "The methodological foundations of lesion network mapping remain sound" www.biorxiv.org/content/10.6...

📢 DGKN 2026: Congress for Clinical Neurosciences with Continuing Education Academy
📅on 25 February 2026, Prof. Dr. Thomas Wolfers
will present a talk titled: “𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗣𝗮𝗿𝘀𝗲 𝗜𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀”
@thomaswolfers.bsky.social #DGKN2026 #ClinicalNeuroscience #BigData #ML

We don't think it invalidates the approach.

We looked into this in our data and replicated the key result showing convergence to non-specific connectome structure. However, our results also suggest there is a practical fix that is already implemented in some LNM studies.

bsky.app/profile/pete...

Many thanks to Geert Jan Biessels, Kaustubh Patil, @sbe.bsky.social, and all colleagues in the Meta VCI Map Consortium (names in the supplement)!

Code is available on GitHub: github.com/umcu-VCI-gro...

Domain-level lesion network maps are on OSF: osf.io/puq36

The takeaway: LNM is still a valuable tool when the statistics are handled correctly. Symptom-label permutation provides a practical fix for the convergence problem, indicating that patient-level connectivity maps do carry genuine, symptom-specific signals that established techniques can miss.

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment Petersen et al. show that assessing the brain circuitry affected by cerebrovascular white matter lesions improves the accuracy with which cognitive perform

Jointly, these results align with our work showing that LNM significantly improves individual-level symptom prediction compared to pure lesion location – something that would not be possible if patient-level lesion connectivity maps would only carry non-specific information.

doi.org/10.1093/brai...

Comparison of label permutation-based lesion network mapping and voxel-based lesion-symptom mapping (VLSM). For each cognitive domain, lesion network mapping (LNM) results thresholded at pFDR < 0.05 (red) are overlaid with VLSM maps (blue). Dice coefficients reported in each panel quantify the overlap between methods after significance thresholding for each outcome. Permutation-based LNM maps showed only partial overlap with VLSM maps for the same outcomes, suggesting that even under label permutation testing, LNM captures effects that are not simply reducible to focal lesion-symptom associations.

Importantly, the label permutation approach did not reduce LNM to focal lesion effects: permutation-based LNM maps only partially overlapped with voxel-based lesion-symptom mapping results for the same outcomes.

Comparison of parametric and label-permutation inference in symptom-linked lesion network mapping. For each cognitive domain, group-level lesion network maps thresholded at pFDR < 0.05 are shown for parametric voxelwise inference (blue) overlaid with label permutation maps (red; appearing darkblue where overlapping; note that no voxels light up red, because all label permutation map voxels overlap with parametric voxels). Dice coefficients report the overlap between parametric and label permutation maps after significance thresholding for each outcome.

This plot demonstrates differences between parametric (blue) and permutation-based maps (red).

Permutation-based inference reduces convergence toward degree-like structure and decreases cross-domain similarity. (a) Symptom-linked lesion network maps derived using label permutation, thresholded at pFDR < 0.05. (b) Pairwise comparisons of label-permutation lesion network maps. Upper triangle: similarity quantified as Dice overlap of significance-thresholded maps. The first row/column compares each map with the normative connectome degree map (degree defined as the row-sum of the normative FC matrix; binary degree mask thresholded at the 95th percentile). Lower triangle: pairwise overlaps of significance-thresholded maps; maps corresponding to columns are shown in blue and rows in red. Diagonal: volumes of thresholded masks (mm³).

Under permutation-based inference, the story changed. Maps became more biologically plausible (e.g., the language network lateralized), cross-domain similarity dropped, and degree-like overlap decreased. Reflecting this stricter null, visuospatial memory lost significance entirely.

How it works: Shuffling symptom labels reproduces the convergence in every permutation. Thus, the artifact becomes part of the null distribution. Under this stricter null, only signals exceeding the artifact remain significant, treating the rest as nonspecific background.

We implemented symptom-label permutation with FSL PALM which just takes one line of code. web.mit.edu/fsl_v5.0.10/...

Mapping Lesion-Related Epilepsy to a Human Brain Network This case-control study evaluates the locations of epilepsy-associated lesions with respect to specific brain regions and networks.

Next, we applied symptom-label permutation, a standard nonparametric strategy in neuroimaging that has also been used in other LNM studies beyond our own (e.g., https:// doi.org/10.1001/jamaneurol.2023.1988).

Parametric symptom-linked lesion network mapping yields highly similar maps across cognitive domains. (a) Symptom-linked lesion network maps derived from parametric statistics, thresholded at pFDR < 0.05. (b) Pairwise comparisons of symptom-linked maps. Upper triangle: similarity quantified as Dice overlap of thresholded maps and Pearson correlation (r) of voxelwise T-statistics. The first row/column compares each symptom-linked map with the normative connectome degree map (degree defined as the row-sum of the normative FC matrix; binary degree mask thresholded at the 95th percentile). Lower triangle: voxelwise scatterplots of T-statistics. Diagonal: volumes of thresholded masks (mm³).

First, we reproduced the “standard” symptom-linked LNM workflow with parametric voxelwise inference as in the critique paper.

As predicted, we observed convergence under this approach. Distinct deficits were associated with nearly identical networks which aligned with the normative degree map.

We tested this formally in multicenter post-stroke cognitive impairment data from the Meta VCI Map Consortium (12 cohorts; n=2,950; metavcimap.org). We statistically compared lesion connectivity maps of cognitively impaired vs. unimpaired stroke patients across 6 distinct cognitive domains.

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment Petersen et al. show that assessing the brain circuitry affected by cerebrovascular white matter lesions improves the accuracy with which cognitive perform

The paper prompted us to revisit our own earlier work, which used symptom-label permutation instead of parametric statistics as commonly used in LNM. We realized this permutation approach provides a straightforward way to address the highlighted convergence problem.

doi.org/10.1093/brai...

As group-level aggregation repeatedly samples the same connectome matrix, different lesion sets produce network maps of implausibly high similarity. Consequently, the authors question whether LNM can identify symptom-specific networks.

We agree that these findings necessitate a reappraisal of LNM.

LNM links focal brain lesions to distributed neural circuits using a normative functional connectome. However, van den Heuvel et al. showed that common LNM procedures introduce a bias that causes maps to converge on nonspecific properties of the connectome, such as node degree.

Investigating the methodological foundation of lesion network mapping - Nature Neuroscience The lesion network mapping method links diverse brain lesions to similar functional brain networks, reflecting general brain organization rather than disorder-specific circuits.

In this work, we address the recent methodological critique on lesion network mapping (LNM) by Martijn van den Heuvel and colleagues (doi.org/10.1038/s415...). We replicate their results in a multicentric sample of 2,950 stroke patients and propose a practical fix to the identified problem!

Revisiting the methodological foundation of lesion network mapping Lesion network mapping (LNM) links focal brain lesions to distributed neural circuits by projecting lesion locations through a normative functional connectome. van den Heuvel and colleagues recently s...

New preprint: Revisiting the methodological foundation of lesion network mapping.

doi.org/10.64898/202...

A thread.