# Toward generalizable brain-trait associations: A multiverse framework for structural MRI

Elise Delzant, Jeremy Lefort-Besnard, Olivier Colliot, Baptiste Couvy-Duchesne

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Elise Delzant, Jeremy Lefort-Besnard, Olivier Colliot, Baptiste Couvy-Duchesne. Toward generalizable brain-trait associations: A multiverse framework for structural MRI. 2025. hal-05400002v1

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Preprint submitted on 5 Dec 2025 (v1), last revised 19 Dec 2025 (v2)

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Elise Delzant¹, Jeremy Lefort-Besnard, Camille Maumet †, Olivier Colliot¹, Baptiste Couvy-Duchesne¹,²,³

1: Sorbonne University, Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France

† Univ Rennes, Inria, CNRS, Inserm, Rennes, France

2: Institute for Molecular Bioscience, the University of Queensland, St Lucia, QLD, Australia

3: QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia

## Table des matières

<table><tr><td>Title ideas</td><td>Érreur ! Signet non défini.</td></tr><tr><td>Introduction</td><td>2</td></tr><tr><td>Materials and methods</td><td>6</td></tr><tr><td>UK Biobank Data</td><td>6</td></tr><tr><td>Brain MRI Processing</td><td>6</td></tr><tr><td>Brain-Wide Association Study</td><td>7</td></tr><tr><td>Same-Data Meta Analysis</td><td>8</td></tr><tr><td>Aligning summary statistics from different processing</td><td>8</td></tr><tr><td>Discovering new regions<br>Significant clusters</td><td>9</td></tr><tr><td>Prediction<br>Significant voxels prediction<br>Best Linear Unbiased Prediction</td><td>10<br>Érreur ! Signet non défini.</td></tr><tr><td>Phenotype and confounders</td><td>Érreur ! Signet non défini.</td></tr><tr><td>Results</td><td>13</td></tr><tr><td>Discovering new regions with SDMA<br>Voxels and clusters<br>ROIs discovered</td><td>13<br>14<br>15</td></tr><tr><td>Predictive power of significant vertices using SDMA</td><td>Érreur ! Signet non défini.</td></tr><tr><td>Optimizing overall prediction</td><td>Érreur ! Signet non défini.</td></tr><tr><td>Conclusion</td><td>19</td></tr><tr><td>Limitations</td><td>22</td></tr></table>
Introduction

The analysis of T1-weighted brain MRI data involves a wide range of available processing pipelines, and the choice of pipeline can substantially influence the resulting outcomes [1]. In functional MRI, a notable study demonstrated how different analysis habits across research teams—applied to the same dataset—led to considerable variability in results [2]. A similar trend has been observed in structural MRI : prior work has shown that the choice of volume- based processing pipeline can impact measurements but also lead to divergent findings, particularly in the identification of associated brain regions [1]. Moreover, recent evidence highlights the important role of preprocessing pipelines in prediction tasks. In a study on T1w MRIs and deep brain age models, the choice of pipeline led to prediction differences of up to 0.75 years [3].

As neuroimaging studies require large datasets to provide reproducible results [4] and since
the range of pipelines possibility is extremely large, exploring all possibility/features is in
practice unfeasible. Comparing the most widely used pipelines is a viable option to provide
guidelines to neuroimagers. In one of our previous studies, we compared 5 widely used
pipelines for a UK Biobank study and compared results according to different metrics such as
global association, ability to detect brain regions, or brain-based prediction performance [5].
We found that there is no unique best processing and that each capture to a degree of unique
signal. Similarly, another study compared brain-age prediction for four different processing
pipelines and found prediction differences depending on the pipeline used[3]. Another study
showed that the choice surface-based preprocessing pipeline had an impact on neuroimaging
cortical surface analyses.

While none of these studies advocate for a particular processing, they all emphasize the
importance of using multiple tools to help neuroimagers make informed decisions before
analysis. Such comparisons are not only computationally costly but also focus solely on
evaluating differences between pipelines, without leveraging the potential of aggregating
multiple pipelines. Moreover, they offer little insight into whether different pipelines share
common signals or how well results from one processing method generalize to another.

Multiverse analysis has emerged as a promising approach to tackle and leverage the variability coming from different neuroimaging processing pipelines. Its underlying idea is to combine outputs from multiple brain representations to derive consensus results while accounting for correlations between pipelines [6]. For instance, one study employed active learning to integrate the outputs of hundreds of pipelines, enabling age prediction and classification of individuals with autism spectrum disorder, while also quantifying the relationships between pipelines [7]. However, this approach is currently limited to small datasets and operates at the region-of-interest (ROI) level.

A recent method developed by Lefort-Besnard et al. [8], known as Same-Data Meta-Analysis (SDMA), proposed combining statistical maps derived from various preprocessing to enhance the reliability of findings and potentially identify novel brain regions associated with the traits of interest. This strategy is particularly appealing as it relies solely on summary statistics (e.g., brain association or activation maps), eliminating the need for individual-level data and facilitating reproducibility and sharing of data/results across research teams. SDMA has shown some advantages but has not been evaluated in its ability to derive more robust prediction.

To address this limitation, ensemble learning offers a powerful solution as it aims to combine
predictors that may be trained on different brain representations or algorithms. This approach
has been particularly effective in improving diagnostic accuracy in contexts such as
Alzheimer's disease by integrating outputs from multiple model architectures [9]. It has also
demonstrated its usefulness to combine seven algorithms to optimize age prediction [10].

However, these methods are limited to the use-case of prediction.

In this study, we used a large dataset of 39,655 brain images from the UK Biobank, each processed using three different volume-based preprocessing pipelines. For each of the four traits of interest (diabetes, maternal smoking around birth, high blood pressure, and frequency of alcohol consumption), we first analysed the associations between the trait and the grey-matter maps produced by each of the three pipelines, yielding 4 × 3 association maps.

We then applied two SDMA approaches to combine the outputs of the three pipelines for each trait, resulting in an additional 2 × 4 combined association maps. To compare the performances of the single-pipeline maps and the SDMA-derived maps, we evaluated several criteria: the number of significant associations, their replication in an independent sample, and their predictive power.

To assess prediction power, we constructed a linear predictor based on the z-scores of voxels that show significant associations. This method serves two purposes: it helps validate the relevance of identified associated brain regions and assesses their predictive utility [11]. It has previously been employed to compare the predictive performance across different traits and preprocessing pipelines, thereby providing insight into their sensitivity for detecting brain-trait associations [5]. However, while being informative in evaluating region significance, this technique may not be optimal when the goal is maximizing predictive accuracy. Therefore, we employed an ensemble learning approach combining signals from all three VBM pipelines and compared its predictive performance.

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# Materials and methods

## UK Biobank Data

We included all UK Biobank participants who had undergone a brain MRI scan at the time of data extraction. Participants were excluded if labelled (by the UK Biobank) as “unusable” due to low MRI image quality, or if they withdrew from the study. Our final sample comprised 39,955 participants, aged 62.7 (SD=7.7) on average with 53% women. The discovery sample consisted of participants from the first center (Cheadle, greater Manchester): 23,288 adults, aged 63.1 on average (SD=7.5) with 52% women. The replication sample consisted of 16,538 adults, aged 62.2 on average (SD=7.7), with 54% women collected across the three other imaging centres (Reading, Newcastle and Bristol).

Informed consent was obtained from all UK Biobank participants. Procedures are controlled by a dedicated Ethics and Guidance Council (http://www.ukbiobank.ac.uk/ethics). IRB approval was also obtained from the North West Multi-Centre Research Ethics Committee. This research has been conducted using the UK Biobank Resource under Application Number 53185.

## Brain MRI Processing and Phenotypes

All 39,655 brain images were processed to extract grey matter representations using two FSL-based processing (FSLVBM and FSLANAT) [12] and volume-based CAT12-ENIGMA [13]
pipelines. Image processing and quality control procedures have been described in detail
previously [5].

We focused our analyses on four phenotypes from the UK Biobank, as they have previously exhibited largest prediction power: Maternal smoking around birth (Data Field ID 1787), Diabetes (Data Field 2443), Frequency of drinking alcohol (Data Field 20414, excluding former drinkers- Data Field 20404) and High blood pressure (Data Field 2966). We specifically focused on these traits because our previous study revealed higher morphometricity and a greater number of associated brain regions for them [5].

## Brain-Wide Association Study

The Brain-Wide Association Study (BWAS) consists in mass-univariate voxel-wise analysis, or VBM. Performed BWAS using the OSCA [14] software with the *-linear* option, while accounting for demographics and imaging covariates. VBM maps include beta scores, standard error, p-value, and N sample size. We computed z-scores as $\frac{\beta}{\text{Standard Error}}$ for each voxel.

As covariates, we included age, sex, intra-cranial volume, a set of imaging covariates (including the assessment center, the head positioning in the scanner, the inverted signal-to-noise ratio, the mean head motion, the discrepancy between T1-brain image and standard-space brain template, the intensity scaling for T1, the time difference since first MRI of the scanner), and a set of body-size covariates (Hip and Waist circumference Body Mass Index) all of them have been confirmed to be associated with grey-matter structure in a previous article [5].
## Same-Data Meta-Analysis

For each trait, we obtained three pipeline-specific VBM association maps (one per preprocessing). We then applied two Same Data Meta-Analysis (SDMA) methods [8] to these maps, chosen to enable a direct comparison of their outputs. Each SDMA method generated its own combined map for each trait, resulting in two additional SDMA maps in both FSL and CAT12 spaces. We combined all three VBM maps using SDMA-Stouffer and SDMA Generalized Least Squares (GLS) approach. The Stouffer method combines the association results from multiple preprocessing pipelines by computing the mean of their voxel-wise Z-scores, while accounting for correlations between pipelines. In contrast, the SDMA-GLS method combines the VBM maps from different pipelines by down-weighting pipelines that are highly correlated, using the inverse covariance structure ($1^tQ^{-1}$). This ensures that redundant pipeline results contribute less to the final combined map than more independent ones. All informations on these methods have been explained in details in a previous paper [8].

## Projecting VBM maps from different processing

Summary statistics from FSL-based pipelines (FSLANAT and FSLVBM) and CAT12 were transformed into both FSL and CAT12 spaces, enabling separate analyses in each space of reference. This step was necessary because CAT12 and FSL generate images with different dimensions (85x103x85 voxels for CAT12 and 91x109x91 voxels for FSL) and used different MNI templates. For each processing, we constructed an average image on 300 participants, sex and center balanced. We utilized ANTs (version 2.0.5) and MRtrix (version 3.0.4) to project from one space to another. First, we registered the average from one processing onto the second average, which generated a transformation matrix (antsRegistration function,
linear interpolation). We applied the transformation matrix to the z-score or BLUP weight
maps of each trait using the mrtransform function (nearest neighbours interpolation), to
obtain the VBM maps in the other space.

As a sensitivity analysis, we evaluated how predictive performance was affected by the choice
of image pairs used to estimate the transformation matrix, as well as by the transformation
methods (linear, nearest neighbour) and interpolation schemes (linear, B-spline, Gaussian)
employed. We additionally applied the FSL mask, transformed it to CAT12 space, and then
back to CAT12 space to evaluate the accuracy of the transformation matrix. Finally, we
estimated the correlation between FSL BLU score registered into CAT12 space and BLU
CAT12 space.

Evaluation of VBM maps combined using SDMA

Significant clusters and replication rate

We reported the number of significant voxels after applying Bonferroni correction for
multiple testing, using thresholds based on the number of voxels for each pipeline: 192,483
for CAT12, 181,544 for FSLVBM, and 184,637 for FSLANAT. We also reported the number
of significant clusters. To identify clusters of contiguous significant voxels, we performed an
iterative 3D cluster search using the *vcgKDTree* function from the R-library *brainMapR*. At
each step, the algorithm considered the 10 nearest neighbours from the set of significant
voxels and included them in the cluster if they were also significant. The algorithm stops
when all significant vertices have been attributed to a cluster.

We reported the replication rate of the findings by performing similar analyses in the
replication sample. As in the discovery analysis, we used the Bonferroni significance
threshold to call an association significant, which is quite stringent, but it simplified the
comparison of results across processing. We also reported the number of replicating clusters
within each region of interest (ROI) and the number of ROIs containing at least one
replicating cluster for each processing. We defined ROIs using the Jülich-Brain atlas v3.0.3
[15] comprising 157 cortical regions, the HarvardOxford atlas [16] that comprise 21
subcortical regions and the Dierdrichsen atlas, which includes 28 cerebellar regions [17].

Prediction accuracy from significant voxels

We evaluated the predictive power of significant voxels before and after applying SDMA. To
do so, we identified significant voxels based on VBM maps, and built a linear predictor using
their corresponding Z-scores. The significance threshold was p< 2.6e-07(=0.05/192483) for
CAT12, p<2.7e-07 (=0.05/184637) for FSLANAT and p<2.8e-07(=0.05/181544) for
FSLVBM.

We used OSCA (–score option) to calculate the brain-based score for each individual in the
testing set. We estimated the prediction accuracy (R²) of these scores in the replication sample
using a linear model that controlled for site effects and neuroimaging covariates (see BWAS
section for full list). We tested the R2 significance using a likelihood-ratio test (nested models,
between a full model that includes predictors and covariates vs. a reduced model with only
covariates), using lrtest function from ‘lmtest‘ R-library. We estimated the standard error
using a bootstrap approach, implemented with boot function from boot R-library.

This analysis was conducted across FSLANAT, FSLVBM, and CAT12, yielding an estimate
of SDMA's predictive power in each space.

Maximizing the prediction: applying SDMA to BLUP weights
Here, we evaluated the performance of SDMA for ensemble learning, and in particular as a
way to optimally average model weights across processing. We applied SDMA to combine
weights from Best Linear Unbiased Predictors (BLUP) that were trained in each processing.
We benchmarked this approach against more traditional ensemble learning (stacking), where a
meta-model combines the predictors trained on the different brain processing.

Best Linear Unbiased Predictors (BLUP)

Best Linear Unbiased (BLU) Predictors is a statistical approach that leverages the entire grey
matter without restricting the model to statistically significant regions. They have been widely
used in animal and human genetics and have previously been applied to voxel-wise data of the
UK Biobank, where they showed superior performance compared to significant-voxels
prediction [5], and same if not slightly better than LASSO method [11]. Unlike machine
learning models, BLU Predictors do not require cross-validation as they rely on maximum
likelihood to estimate the shrinkage parameter. To limit overfitting that may occur in high
dimension (more voxels than individuals), BLUP shrinks individual effect estimates toward
the mean and combines information from all predictors, which helps maintain accurate
predictions.

BLU Predictors are efficiently implemented in OSCA.

BLUP weights were trained on ~23,000 individuals, and we will evaluate the prediction
accuracy in the test sample of 16,000 individuals.

Transferability of prediction across processings

As a baseline, we evaluated the generalizability of the prediction across processing
pipelines—for example, by testing the predictive power of FSLVBM-derived brain maps
projected into CAT12 space. Conversely, we estimate the predictive power of CAT12 maps onto FSL processings. We further assessed the generalizability of prediction accuracy between the two FSL-based pipelines (FSLANAT and FSLVBM), which do not require projection. Given that both originate from the same software suite, are in the same space, and have previously been shown to share substantial signal [5], we hypothesized that the prediction would generalize well between them. As per the cross-prediction between FSL and CAT12 spaces, our previous work showed that the different processing captured some common trait variance, supporting the hypothesis that at least part of the signal would transfer.

### Applying SDMA to BLUP weights

Rather than combining Z-score brain maps as in the initial approach, we combined maps of BLUP weights derived from FSLANAT, FSLVBM, and CAT12. We compared the results from the SDMA GLS and SDMA Stouffer methods, for all 4 traits included in this analysis. We evaluated the prediction accuracy of the SDMA combined BLUP against the prediction from each individual BLU Predictor trained on a single processing.

### Stacking

Finally, to improve prediction accuracy, we implemented a stacking approach by combining BLU scores derived from all three volume-based processing pipelines. We assigned weights to each pipeline based on their relative uniqueness: we first computed the pairwise correlations between pipelines, then calculated a uniqueness score for each as one minus its average correlation with the others, and finally normalized these scores so that each weight reflected the proportion of unique signal contributed by that pipeline. By averaging these scores for each subject—while accounting for correlations between pipelines—we generated a
predictor aggregating signal from all three pipelines. We then evaluated its performance
across test sets from each individual processing, aiming to leverage complementary
information captured by different pipelines.

Results

Sensitivity analysis

We observed that (Supplementary Figure 1), except when using MNI or BLU scores directly,
the choice of input or interpolation has minimal impact on the prediction. Therefore, for the
following analyses, we used a transformation matrix estimated from an average input with
linear interpolation. We also observed a correlation of 0.995 between the FSLVBM grey-
matter mask and the mask obtained after transforming it into CAT12 space and then back to
FSLVBM space. Finally, across the four traits of interest, we observed an average correlation
of 0.65 (SD = 0.045) between the FSLVBM BLU scores mapped into CAT12 space and the
CAT12 BLU scores.

SDMA identifies novel clusters and brain regions
<figure><img src="image_2.png" /><figcaption>**Figure 1:** Results obtained after SDMA-multiverse analysis, against results from a single processing.</figcaption></figure>

Analyses were performed using different processing approaches (FSLVBM, FSLANAT, and CAT12). The spider plots display the performance of analyses from a single processing, vs. SDMA according to six evaluation metrics. In each plot, colors represent different methods: blue for the processing pipeline alone, pink for SDMA GLS, and yellow for SDMA Stouffer. All metrics were computed across four UK Biobank phenotypes. Axes scales are as follows: number of significant voxels (range 0 to 8,500), percentage of replicating voxels (range 0% to 50%), number of significant clusters (range 0 to 800), percentage of replicating clusters (range 0% to 30%), number of significant ROIs tagged by replicating clusters (0 to 110), and percentage of correlation between predicted values and true values (0% to 17%).

## Voxels and clusters

Since SDMA GLS and SDMA Stouffer performed similarly, we only reported results for SDMA GLS in the main text, but kept both for figures and tables.
We evaluated SDMA by first estimating the number of significant voxels associated with each trait for each approach. While SDMA increased the number of significant voxels compared to FSLVBM and FSLANAT (Figure 1), it decreased it for CAT12. A possible explanation is the much larger clusters exhibited by CAT12 compared to FSL pipelines, a pattern consistent with previous findings showing that CAT12 exhibits high inter-voxel correlations, often resulting in large contiguous clusters of significant voxels [5]. For each processing, SDMA resulted in roughly similar voxel-replicating rate (~35-40%).

However, when focusing on the number of significant clusters, SDMA increased it for each VBM pipeline, as well as its cluster-replication rate. Notably, SDMA resulted in a replication rate of 21% compared to 5% for CAT12 alone. Moreover, SDMA identified replicating clusters in ROI not previously detected, given the larger number displayed on Figure 1.

Finally, the correlation between predicted outcomes and true values, did not improve using SDMA. For instance, the correlation was around 17% for both SDMA and FSLVBM itself (Figure 1). Results were similar for CAT12 and FSLANAT. This can be explained by new vertices identified with SDMA tagging same signal as the one identified when using processing alone.

Overall, SDMA methods proved beneficial by consistently increasing the number of detected clusters, and the replication rate of clusters across all pipelines.

## The choice of the space of reference influences results

We found that depending on the space of reference, the number of clusters and ROI varied (Figure 1). We sought to investigate these differences focusing on Diabetes trait, for which we had the most significant ROIs. We contrasted ROIs identified by each analyses of a single
processing with the SDMA results performed in FSLVBM space of CAT12. Indeed, SDMA
results in FSLANAT and FSLVBM reference space were highly similar (85% of overlapping
ROIs), we did not present results for FSLANAT in the main text.

We found that the choice of output space had an effect on the results: for example, in the case
of `Diabetes` (Figure 2), SDMA GLS in FSLVBM revealed significant effects in multiple
subcortical structures, including the left thalamus, right caudate, right thalamus, and right
amygdala, whereas no subcortical findings were observed with SDMA GLS in CAT12.
Nonetheless, both mappings identified a small number of overlapping regions in the
cerebellum. Overall, SDMA in FSLVBM reference space identified 34 regions while SDMA
in CAT12 reference space identified 26 ROIs. For SDMA in FSLVBM space, 26% were
overlapping and 74% were unique, while in CAT12 space, SDMA identified 35% overlapping
regions and therefore 65% unique regions.

For `maternal smoking` (SFigure 1) and `Alcohol frequency` (SFigure 2) trait, 14% of
regions discovered in FSLVBM space overlapped (e.g. 3 ROIs), corresponding to 21% in
CAT12 space. For `High blood pressure` (SFigure 3) 4 ROIs out of the 7 discovered in
FSLVBM space replicated (vs. 51 discovered in CAT12 space).

These findings underscore the fact that the selection of projection space can substantially
impact regional detection.
<figure><img src="image_3.png" /><figcaption>**Figure 2: Location of replicating clusters per region of interest, for Diabetes.** The first three columns show regions tagged by significant clusters from analyses performed within an individual processing pipeline (CAT12, FSLANAT, and FSLVBM), while the two final columns show regions identified by SDMA method in both FSLVBM and CAT12 reference spaces.</figcaption></figure>

## SDMA for stacking

We used SDMA to combine Best Linear Unbiased Prediction (BLUP) weights, which we compared to BLUP prediction achieved from a single processing, and that obtained via stacking of the different BLU Predictors.
<figure><img src="image_4.png" /><figcaption>**Figure 3 :** Effect of SDMA or stacking on prediction accuracy.</figcaption></figure>

The y axis shows the increase/decrease prediction R2 of the different approaches, compared to the reference prediction (BLUP from a single processing evaluated in the same space). The different panels correspond to the reference spaces. Each boxplot shows the prediction ratio across the 4 traits considered. For example, in panel 1, prediction accuracy is evaluated the space of FSLANAT and BLUP scores trained on FSLANAT (red) are used as reference (ratio of 1). BLUP predictors trained on other spaces (FSLVBM, and even more pronounced CAT12) exhibit a prediction ratio <1, which indicates a reduced prediction accuracy compared to the reference. SDMA combined BLUP recover some of the drop in prediction but remains lower than the reference, while stacking improves prediction accuracy by about 10% on average.

We observed that predictors trained in a different space showed reduced performance (as seen in **Figure 3**), though they remained statistically significant (p < 0.05 / (18 × 4)). While p-values are not displayed in the plot, all were statistically significant. Predictors trained on FSL-based measurements and applied to phenotype prediction in the CAT12 test space showed decreased performance, with reduction rates of 0.85 and 0.77 (third column, red and gold boxes, **Figure 3**). This suggests that while performance drops, a portion of the signal
remains transferable across processing pipelines. Similar results were observed for other reference spaces.

Across all pipelines, the Same Data Meta-Analysis (SDMA) methods (both GLS and Stouffer) was still lower than the reference but much better than worse performing single predictor (Figure 3, both blue boxes). For example, SDMA in FSLVBM space yielded a median decrease rate of 0.81; similar trends were observed in FSLANAT and CAT12, with SDMA reaching up to 0.95.

Ultimately, the stacking approach outperformed all other methods, showing median increase rates greater than 1 across all scenarios, with the most significant gain observed for CAT12 (pink boxes, **Figure 3**). Specifically, it achieved a 60% improvement over the SDMA method in this case.

Overall, these findings indicate that while each processing method captures some unique aspects of the signal, there is also a transferable part across methods. Moreover, SDMA approach acts as a consensus but is outperformed by stacking.

# Conclusion

## Some part of the signal is transferable across methods

We found (Figure 3) that transferring results from one VBM space to another lowered predictive performance, although some part of the signal was still preserved. This matters for meta-analytic approaches that combine maps from different processing pipelines, such as IBMA [18], which show that pooling information across studies can improve the stability of results. Similar findings come from large-scale work on analytic flexibility in fMRI [2], where combining results from many analysis workflows produced more reliable activation patterns.
Together, these observations support the idea that integrating results across pipelines can
strengthen neuroimaging conclusions.

SDMA enables novel discoveries and improves replication rate

We found that both SDMA GLS and SDMA Stouffer worked the same in our data (Figure 1),
which was somewhat expected from theory when the scenarios do not have outliers. In this
study, SDMA GLS was slightly more powerful, yet not by a lot, which also aligns with the
theory.

We demonstrated that both SDMA GLS and SDMA Stouffer achieved replication rates—at
both the voxel and cluster levels—that were comparable to, or even higher than, those of
single processing methods, with CAT12 showing a notable increase in replicating clusters
from 5% to 22% (Figure 1). SDMA in the FSLVBM space showed an increase from 4% to
8%, with similar results for FSLANAT. These relatively low figures are expected, as they
reflect replication assessed at the brain-wide level.

SDMA does not improve prediction from significant regions

We found that (Figure 1), SDMA did not increase the prediction power, though it improved
the detection of redundant brain regions. This is still important to keep in mind as it can help
drawing a more accurate picture of the networks associated with a trait. However, SDMA
does not increase the statistical power in the same way as a classical meta-analysis (e.g. it
does not boost detection of regions with smaller effects). Indeed, this result is not unexpected
as we used three different brains representations but of same individuals, which is different
from increasing the sample size.
SDMA helps combine results across processing but the choice of the space of reference impact results

We found that (Figure 1) the number of clusters vary widely, depending on the reference space used (~800 in CAT12 reference space vs. ~700 in both FSL-based reference spaces). Moreover, the number of overlapping ROIs discovered by SDMA (across the reference space) remains low : for `Diabetes` trait around 35% of ROIs discovered by SDMA in CAT12 space vs. 26% in FSLVBM space. Some of this discrepancy could be attributed to the method we use to realign VBM maps, but in practice it also shed light on the difficulty to interpret SDMA outputs.

SDMA can be used for model averaging but remains less performant than stacking

To optimize predictive power, we used a stacking approach, aggregating BLU prediction scores from all three-based processing, while taking into account for their inter-pipeline correlation. We found that stacking systematically increased prediction rate (Figure 3), compared to processing alone or SDMA methods. Therefore, stacking is a powerful approach when the goal is to improve prediction accuracy. However, BLUP does not operate on summary statistics and requires access to images processed through all pipelines, which can be computationally demanding. Moreover, stacking may reduce interpretability as it is less direct to identify the brain regions driving the predictions. Ultimately, predictive models based on significant voxels represents a trade-off—sacrificing some prediction accuracy in favor of greater interpretability.

Clinical regions discovered by SDMA in FSLVBM reference space
We found that applying the SDMA approach led to the identification of a greater number of brain regions associated with various traits, alongside improved replication rates (Figure 1).

We focused our results on regions identified by SDMA in FSLVBM reference space, as this processing pipeline has previously demonstrated to be a performant all-rounder for analyses in the UK Biobank [5]. Notably, for the Diabetes trait, ten additional regions (than the one obtained with FSLVBM only) were identified within the cerebellum (Figure 2). This finding aligns with prior research indicating that cerebellar grey matter is significantly affected in the progression of Diabetes [19], and that cerebellar-cerebral circuits may play a role in the disorder [20]. Additional evidence has also linked the amygdala activity to Diabetes[21] and to recurrent hypoglycemia—a common complication of both types of Diabetes [22]. For maternal smoking around birth, the SDMA analysis uncovered a novel cerebellar association (Left_V, SFigure 1). This may be linked with a previous review reporting that prenatal exposure to maternal smoking is associated with reduced cerebellar volume [23].

Overall, these findings are yet interesting but do not necessarily replicate in other reference space and more work is needed to investigate the direction of effect.

## Limitations

While our results show that combining multiple processing pipelines using the SDMA approach enhances the discovery of brain regions associated with the trait, a key limitation is that different processing methods produce outputs in different anatomical spaces. This necessitates realignment of the statistical maps and requires an arbitrary decision about which space to use as the reference for SDMA outputs. This choice can influence the results, potentially introducing variability depending on the selected output space (Figure 2). We used mrtrix and ANTs for projection of association maps across spaces, which we validated
(using prediction that remained significant after projection). However, there may be other
projection strategies that may lead to more stable SDMA results across multiple spaces. As
one could think SDMA would help unify results from different processing, the choice of
reference space also induces variability.

However, to facilitate reproducibility, we will share all association maps, as well as the code
we used for the analyses and the average images used to compute the transformation matrices
between spaces.

Additionally, this study focuses exclusively on the combination of volume-based processing
pipelines and does not incorporate surface-based approaches. Given known discrepancies
between volume- and surface-based results [5], integrating both could provide valuable
insights. But, combining volume- and surface-based approaches remains a methodological
challenge : we investigated the use of surf2vol from FreeSurfer but it did not generate
satisfying volume-based images. In addition, surf2vol is only implemented for the cortex, and
not for subcortical volumes; moreover, the cerebellar area is not available in surface -based
processing thereby limiting the completeness of the analysis. Nevertheless, SDMA is the first
published method that enables such combination of statistical maps, and given the variability
reported across volume-based methods [1], focusing on their combination remains a valuable
step toward improving reproducibility in neuroimaging analyses.

A potential limitation of this study is that all analyses were performed exclusively on the UK
Biobank dataset, focusing on only four traits and three volume-based processing pipelines.
Consequently, the generalizability of our findings to other datasets, traits, or preprocessing
methods remains to be confirmed. Nonetheless, the high degree of homogeneity within the
UK Biobank suggests that our results are likely to extend to additional traits within this
cohort, representing a promising direction for future research. Furthermore, more associations
maps are shared (on NeuroVault platform for instance, including that from our previous work[5]), which will help apply and test SDMA in many settings.

Finally, one limitation not previously discussed is the environmental cost associated with multi-pipeline analyses. These ensemble learning approaches require processing and storing images multiple times—once for each pipeline—which increases computational demands and, consequently, carbon emissions. For example, processing 40,000 images using FSL generates approximately 200 kg CO₂ eq in France, but as much as 3.5 t CO₂ eq in Australia due to differences in energy production. This raises important questions about the trade-off between environmental impact and relatively modest gains in predictive performance (e.g., a 20% increase using a stacking approach). Nonetheless, these considerations underscore the importance of developing harmonized, reproducible pipelines that minimize redundant computation while maintaining scientific rigor.

<figure><img src="image_5.png" /><figcaption>SFigure 1: Comparison of prediction R² values for FSL BLU scores mapped to the CAT12 reference space using different registration methods (antsRegistration) and transformations (mrtransform). The input denotes the pair of images used to compute the registration matrix. "BLUP to BLUP" indicates direct registration of FSLVBM BLU scores into CAT12 space using antsApplyTransform. "CAT12 to CAT12" represents the prediction of CAT12 BLU scores and serves as the reference.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>SFigure 1: Location of replicating clusters per region of interest, for Maternal smoking.
The first three columns show regions tagged by significant clusters from analyses performed within an individual processing pipeline (CAT12, FSLANAT, and FSLVBM), while the two final columns show regions identified by SDMA method in both FSLVBM and CAT12 reference spaces.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>SFigure 2: Location of replicating clusters per region of interest, for Alcohol frequency.
The first three columns show regions tagged by significant clusters from analyses performed within an individual processing pipeline (CAT12, FSLANAT, and FSLVBM), while the two final columns show regions identified by SDMA method in both FSLVBM and CAT12 reference spaces.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>SFigure 3: Location of replicating clusters per region of interest, for High blood pressure. The first three columns show regions tagged by significant clusters from analyses performed within an individual processing pipeline (CAT12, FSLANAT, and FSLVBM), while the two final columns show regions identified by SDMA method in both FSLVBM and CAT12 reference spaces.</figcaption></figure>