# Balancing Cohort Size and Variability in Iterative Deep Brain Template Creation

Dorian Vogel, Vittoria Bucciarelli, Marc Jermann, Teresa Nordin, Jérôme Coste, Jean-Jacques Lemaire, Karin Wårdell, Simone Hemm

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Dorian Vogel, Vittoria Bucciarelli, Marc Jermann, Teresa Nordin, Jérôme Coste, et al., Balancing Cohort Size and Variability in Iterative Deep Brain Template Creation. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2025, 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.1-4. 10.1109/EMBC58623.2025.11253968. hal-05400683

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Submitted on 5 Dec 2025

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# Balancing Cohort Size and Variability in Iterative Deep Brain Template Creation.

Dorian Vogel¹*, Vittoria Bucciarelli¹, Jérôme Coste², Jean-Jacques Lemaire³ and Simone Hemm¹

Corresponding author: * dorian.vogel@fhnw.ch

1 Institute for Medical Engineering and Medical Informatics, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland

2 SIGMA Clermont, Institut Pascal, Université Clermont Auvergne, Clermont-Ferrand, France

3 Service de Neurochirurgie, Hôpital Gabriel-Montpied, Centre Hospitalier Universitaire de Clermont-Ferrand, Clermont-Ferrand, France

## Abstract

This study investigates the effect of cohort size on the creation of cohort-specific deep brain anatomical templates for movement disorder populations. Preoperative MRI data from 70 patients implanted with deep brain stimulation (DBS) systems were used to generate anatomical templates with varying cohort sizes (5 to 67 subjects). An iterative non-linear normalization pipeline was employed to optimize template generation. Template variability was assessed using Dice overlap of anatomical structures. The templates created with 44 subjects achieved optimal balance between variability and accuracy. Tukey's HSD test confirmed significant differences across iterations and cohort sizes. This study underscores the importance of cohort size and iterative registration methods in creating high-quality anatomical templates.

## Clinical relevance

The findings provide insights into the optimal cohort size for creating anatomical brain templates.

## I. BACKGROUND

Deep brain stimulation (DBS) is a neuromodulation method offered as supplement to pharmalogical treatment to patients as movement disorder such as Parkinson's disease (PD), essential tremor (ET) or dystonia progress. Electrical stimulation is delivered to structures of the deep brain responsible for movement regulation by means of one or several multi-contact electrodes. The surgical implantation itself is challenging, as the size of the target structures and their depth below the surface of the skull makes them hard to reach precisely. Moreover, the pre-operative planning and the post-operative follow-up phases are both tainted with uncertainty as the exact mechanisms of DBS are not fully elucidated. In the former, because the direct effect of the stimulation at a certain location in the brain cannot be predicted, the neurosurgeon has to take into consideration a vast number of parameters based on literature and experience. In the latter, despite knowing the actual location of the electrode in the brain of the patient, the neurologist still has to test numerous combinations of electrical settings and observe the symptomatic effect. One of the avenues to improve this situation is retrospective statistical analysis of stimulation results with the associated
effect relative to the anatomy. In short, patient pre- and post-operative MR and CT
images are used together with electrical stimulation settings and their effect to model
the extent of stimulation within the patient's brain (so-called volumes of tissue
activated, VTA). The data from several patients is then normalized to a common space
to interpret the anatomical location of areas significant for effect. This has been
pioneered by Nowinski and colleagues [7] and has seen very intense development
since [3], [2], [5], [6], [9].

Instrumental in this field is the anatomical normalization step and the choice of
anatomical reference (template) used to fuse the patient cohort studied to. Most studies
use the ICBM2009b-NLIN-ASYM T1/T2 template from the Montreal Neurological
Institute [4] which was created from 152 heathy individuals aged 18 to 44. However,
cohort-specific templates have the potential to offer a better anatomical reference by
matching age and imaging settings for very fine analysis on specific cohorts. When
creating cohort-specific templates, the first parameter is the number of subjects to
include to compromise individual variability and loss of finer details due to excessive
number of subjects. Some studies have explored the question in the case full-brain
templates, quantifying the variation of template quality with subject count [10].

The goal of this study was to explore the influence of the number of subjects in the
creation of deep brain templates for movement disorders populations.

## II. METHOD

The general approach to address the goal of this study was to generate a set of
template with varying number of subjects included from a cohort with rich manual
segmentation data and use the segmentation masks as markers for quantifying the
normalization quality.

### A. Clinical Data

Preoperative MRI data from 70 patients implanted with DBS systems between 2008
and 2018 at the university hospital Clermont-Ferrand (France) was used in this study.
From the 70 patients, 44 were diagnosed with PD from which 40 were targeted in the
subthalamic nucleus (STN) and 4 in Ventrointermediate nucleus of the thalamus (Vim),
24 with ET all implanted in Vim and 2 patients had mixed indications and were
implanted in Vim. Routine preoperative imaging consists in T1 acquisition as well as
WAIR (White-matter attenuated inverse recovery). During planning of the surgery,
between 11 and 20 deep brain structures are labeled per hemisphere. 1 STN-PD, 1
STN-Vim and 1 ET-Vim patient were excluded due to a missing MR sequence.

### B. Anatomical Normalization

Preoperative MR images were used in an iterative non-linear normalization pipeline to
create group-specific anatomical templates with increasing number of subjects.
Patients were grouped based on the anatomical structures available for each, resulting
in groups of 5, 15, 44, 50 and 67 subjects. For each patient, both MRI sequences were
corrected for bias field inhomogeneity and skullstripped based on the T1. The
anatomical normalization workflow was identical for the different template runs. The
first step is the creation of an initial group template using the ICBM2009b-NLIN-ASYM
template [4] as a reference and affine registration using FSL on the T1 modality. A
WAIR reference was created by applying the transforms to the WAIR sets. In the
second step, original data was registered again to the pair of initial references using
ANTS-SyN non-linear registration.

The resulting warps were averaged between T1 and WAIR, then among subjects and
down scaled to create a new pair of references. This non-linear template updating
process was repeated 5 times with coarse registration settings and 4 times with fine
settings from [8]. The process is depicted in *Figure 1*.

In the process, the anatomical structures were transformed as binary masks to each
template run (patient count and iteration) and used as markers for the variability
analysis.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Anatomical normalization and benchmarking workflow for a single template. This process has been repeated 5 times for each cohort size. All patients are first aligned with an affine registration to the ICBM2009b- NLIN-ASYM template, and the resulting images are averaged to create a first reference. Then the T1 and WAIR images of each patient are nonlinearly registered to this reference with ANTS-SyN and the resulting warp fields averaged between sequences to warp both images. A new reference is then generated, and the process can be repeated. In total, the non-linear registration process has been repeated 5 times with coarse settings and 4 times with fine settings.</figcaption></figure>
### C. Variability Analysis

To access normalization quality, the pairwise agreement of anatomical structures outlines between subjects in template space was used. For each pair of the same label from two subjects in the same template run, the dice coefficient (DC) was computed. Only scores for STN, substantia nigra and the lateral geniculate body were used for the analysis.

The DC scores are presented as whisker plots that present the interquartile range (IRQ) for each iteration across the different template subject count. The median value and interquartile range for each run were summarized into overall scores by normalizing both metrics and using the difference.

The importance of the different parameters of the iterations (number of subjects, number of iterations, registration settings) was estimated using Tukey's honestly significant difference test in a pairwise fashion between all combinations templates the share of significant tests is presented as heatmaps, first by subject count (comparing the 9 iterations between two subject counts) and then by iteration (comparing the 5 subject counts for the same iteration).

Finally, visualizations of the coronal and sagittal slices of the two best templates are presented.

## III. RESULTS

*Figure 2* presents the evolution of dice coefficient scores. The highest increase in the DC scores is between the first and the second iterations with the Coarse settings, and between the last Coarse and the first Fine setting. After the second iteration with the Coarse settings, the median and IQR stabilize for all group sizes but the 15 patient atlas which has a notable decrease of the median DC for the Coarse 3 and Coarse 4 iterations, highlighted by the drop in the IQR score. Overall, the 5 subjects templates present a higher median DC score, but higher IQR range. The normalized IQR score reaches maximum at the Fine 2 iteration for with 5 subjects included, Fine 0 with 15 subjects included, Fine 3 with 44 subjects included and Fine 1 with both 50 or 67 subjects included.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Dice overlap scores (top) and Dice IQR score (bottom) across iterations for increasing number of subjects included in the template.</figcaption></figure>
**Figure 3** presents heatmaps of the pairwise comparisons of the distributions of DC values. Templates produced with 5 subjects did not present scores significantly different to others, despite the higher median DC values observed in Figure 2. The templates exhibiting the most share of significant comparisons with templates with other subject count were the templates created with 44 subjects, with 72.2% when comparing templates created with 44 subjects between each other, indicating a significant evolution of the scores over the different iterations (Coarse 0 ... Fine 3). When comparing the iterations, we observe the highest share of significant differences when comparing Coarse to Fine iterations, with Fine 2 presenting shares of significant differences inferior to the three other Fine iterations.

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Share of pairwise Tukey HSD tests on the values of DC rejecting the null hypothesis of non-significant difference. Top: when comparing templates between subject counts; bottom: when comparing templates produced by the same iterations with different subject counts.</figcaption></figure>

The visualization of axial and coronal slices of the top templates with the three highest scores is presented in Figure 4. The 15 subjects template provides both highest noise and contrast, while the template with 50 subjects opposite, with less noise (more homogenous intensities) but also lower contrast (smoother transitions). The 44 patient template lies in between.
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Axial and coronal slices of the two MRI sequences of the three best anatomical templates created. In each tile, the left and right halves are t1 and WAIR respectively. Slices were taken at the largest cross-section of the subthalamic nucleus in each template.</figcaption></figure>

## IV. DISCUSSION

This study investigated the effect of cohort size for the creation of group-specific anatomical deep brain templates. The three best templates were 15 subjects-Fine 0, 44 subjects-Fine 3 and 50 subjects-Fine 1 as the first present the highest variability across iterations with a strong drop in the IQR score for the Coarse 3, the 44 subjects-Fine 3 would be preferred. This is supported by the results of Tukey's test, with 44 subjects and Fine 3 presenting the most significant differences. The results highlight the advantage of the iterative approach introduced by [1] and the multi-stage Coarse / Fine settings we established in [8], with the most significative change in the DC appearing at the change from the first setting to the second. The normalized IQR score helped to compare the different iterations and cohort sizes, with varying results confirming the strong impact of the latter.

These results provide a less definitive answer than those from [10] who identified 200 as the optimal cohort size, resulting in less than 5% variation in their metric. This can be attributed to two of the shortcomings in the present study: first, we only used metrics on the binary outlines, which is inherently subject to variability due to trilinear interpolation followed by binarization after transformation to template space. Second, the normalization process, while taking advantage of the T1 and WAIR MR sequences, with the latter designed to increase contrast in the deep brain, could be improved by using labels outlines to guide the registration.

Further investigations will include label-based registration as a third modality in the normalization pipeline and the integration of more sensitive registration metrics for benchmarking.
V. CONCLUSION

Real-world clinical data from patients implanted with DBS were used in this study to create anatomical templates with increasing number of individuals included, this allowed to identify 44 subjects as an optimal number of subjects and highlighted the efficacy of the iterative normalization method.

ACKNOWLEDGMENT This work was supported by the Swiss National Science Foundation (Grant No. 205320-207491).

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