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# Mind the Brain Age: How Segmentation and Template Selection Reshape Structural Connectomes

Carlo Ferritto, Giulia Lioi, Pierre-Yves Jonin, Julie Coloigner

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Carlo Ferritto, Giulia Lioi, Pierre-Yves Jonin, Julie Coloigner. Mind the Brain Age: How Segmentation and Template Selection Reshape Structural Connectomes. 2026. hal-05446129

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# Mind the Brain Age: How Segmentation and Template Selection Reshape Structural Connectomes

Carlo Ferritto,¹# Giulia Lioi,² Pierre-Yves Jonin,¹,³ Julie Coloigner¹# *

¹Univ Rennes, CNRS, Inria, Inserm, IRISA UMR 6074, EMPENN — ERL U 1228, F-35000 Rennes, France

²IMT Atlantique, Lab-STICC UMR CNRS 6285, F-29238, Brest, France

³Neurology Department, Rennes University Hospital, Rennes, France

# Correspondence: carlo.ferritto@inria.fr, julie.coloigner@irisa.fr

December 18, 2025

**Keywords:** Structural connectome, Tractography, Brain Aging

**Abstract:** Diffusion-weighted MRI samples the directional diffusion of water in vivo, and tractography uses this information to reconstruct brain fiber pathways. Mapping streamlines to an anatomical parcellation yields structural connectomes. However, in older adults, where white matter alterations and atrophy are common, the choice of reference template and tissue segmentation could be particularly consequential for obtaining accurate, interpretable structural connectomes. Using a conventional pipeline, we evaluated how these two factors, the normalization template and the segmentation used to constrain anatomically guided tractography, affect connectome estimates in cognitively healthy elderly participants. These methodological choices produced systematic and significant differences in network topology and their relationships with clinically relevant variables across standard connectome measures, particularly in patients with white matter hyperintensities. Age-appropriate normal-

*Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf*
ization and lesion-aware anatomically constrained tractography yielded networks with more plausible anatomy and more consistent relationships with cognition and imaging measures, whereas unconstrained tracking inflated density without improving interpretability. These findings demonstrate that structural connectome outcomes are contingent upon template and segmentation selection. We therefore advocate for explicit reporting of these methodological parameters and recommend the use of age-appropriate templates combined with white matter lesion-aware segmentation incorporating anatomically informed constraints.

# 1 Introduction

Diffusion-weighted imaging (DWI) infers the orientation of water diffusion within brain tissues, enabling in-vivo characterization of white matter (WM) microstructure (Alexander et al., 2019). Exploiting the anisotropic water diffusion in WM, tractography algorithms reconstruct trajectories that approximate WM architecture, offering a mesoscale scaffold on which to study brain organization (Jeurissen et al., 2019; Takemura et al., 2024).

Despite substantial progress, community challenges and systematic reviews show that current tractography algorithms outcomes remain highly sensitive to sequence acquisition parameters, diffusion models, and algorithm choices (Schilling et al., 2021), with both false positives and false negatives remaining key concerns (Maier-Hein et al., 2017; Schilling et al., 2019; Thomas et al., 2014). For better controlling false positive connections, complementary approaches have been proposed, including signal-consistency filtering (Daducci et al., 2015; Smith et al., 2015), improved orientation modeling (Jeurissen et al., 2014) and bundle specific priors (Durantel et al., 2025; Rheault et al., 2019). Among these, anatomically constrained tractography (ACT) leverages tissue segmentation to enforce biologically plausible seeding and termination rules (Smith et al., 2012). The ACT algorithm has become widely adopted in large scale studies (Mansour L. et al., 2023; Wainberg et al., 2024) and recommended in modern pipelines (Tahedl et al., 2025). Empirically, ACT increases anatomical plausibility relative to unconstrained tracking (Yeh et al., 2016) and has shown good performance in recent community challenges (Girard et al., 2023), supporting current recommendations for its use in tractography (F. Zhang et al., 2022).

Structural connectomes are obtained by mapping WM pathways to an anatomical atlas so that regions become nodes and streamlines become edges, yielding undirected graphs on which global and nodal topological properties can be computed (Sotiropoulos & Zalesky, 2019; Yeh et al., 2021). These graph measures offer a compact summary of brain organization and allow comparisons across cohorts (Meskaldji et al., 2013). However, both global and nodal properties are sensitive to methodological choices in
connectome definition and construction (Rubinov & Sporns, 2010; Wijk et al., 2010), this warrants
explicit reporting and careful interpretation.

The study of structural connectomics is particularly valuable in aging research, where subtle WM integrity
changes can facilitate the differentiation between older adults who will undergo pathological cognitive
decline and those who will not, for example in the context of Mild Cognitive Impairment, but also
prior to the emergence of clinically detectable impairment (Berlot et al., 2016; Peraza et al., 2019).
For instance, lower efficiency and longer paths have been related to worse cognitive performance in
older adults and prodromal disease (Boot et al., 2020; Fischer et al., 2021; Lawrence et al., 2014). A
converging literature also links cerebrovascular burden, most commonly indexed by the load of white
matter hyperintensities (WMH), to connectivity disruption (Wardlaw et al., 2015). In late life, WMH
are widespread and have been linked with reduced structural network efficiency and altered topology,
providing a plausible pathway to cognitive decline (Cai et al., 2021; Rudolph et al., 2024; Taghvaei et al.,
2024; Yang et al., 2020). Crucially, however, these topological measures are highly sensitive to choices
made during network modeling (De Reus & Van Den Heuvel, 2013).

Connectome reconstruction relies heavily on the anatomical accuracy of tissue segmentation to define
streamline start and stop rules. This is particularly critical for ACT, where mislabeling tissue types
directly alters streamline trajectories and terminations. In aging brains, standard intensity-based seg-
mentation tools like FSL FAST (Y. Zhang et al., 2001) often misclassify WMH as gray matter (GM).
This error causes ACT to prematurely terminate streamlines at lesion sites, potentially creating artificial
network disconnections (Theaud et al., 2018). Conversely, methods incorporating probabilistic tissue
priors, such as ANTs Atropos (Avants, Tustison, Wu, et al., 2011), may stabilize segmentation in these
heterogeneous regions, thereby preserving the continuity of white matter tracts involved in pathological
processes. Parallel to segmentation challenges, the choice of reference template for spatial normalization
introduces another source of bias. While the young adult Montreal Neurological Institute (MNI) template
(Fonov et al., 2009) remains ubiquitous, age-specific templates can reduce registration bias by better
capturing cortical thinning, ventricular enlargement, and other features of older brains (Fillmore et al.,
2015). The MIITRA project (Ridwan et al., 2021) provides high-resolution T1-weighted (T1w) and
diffusion-tensor templates specifically for non-demented older adults, with evidence of improved anatom-
ical representativeness and sharpness relative to general templates. In summary, we hypothesize that
the combined use of intensity-based segmentation and standard, young-adult templates fails to account
for the specific morphology of the aging brain, leading to systematic artifacts in structural connectivity.
Therefore, adopting lesion-aware segmentation strategies and age-appropriate templates is essential to
recover accurate and biologically plausible network topologies.
In this study, we systematically evaluate how the tissue segmentation (ANTs Atropos versus FSL FAST) and the reference template (MIITRA versus MNI) used for spatial normalization affect structural connectome estimates in cognitively normal older adults. Analytically, we proceed in three steps: (i) within each pipeline, we quantify template effects on global network measures; (ii) we test whether the relationships between the structural connectome properties estimates and four clinically relevant variables related to imaging and cognitive factors differ among pipelines; (iii) we assess template effect on nodal measures, summarizing spatial coverage and network level patterns. Taken together, this design aims to assess how template selection and segmentation choices propagate to graph topology and their relationships with clinically relevant variables in a sample of healthy elderly participants.

# 2 Materials and Methods

## 2.1 Data

Data were obtained from the Alzheimer's Disease Neuroimaging Initiative<fnref n="1" /> (ADNI), with only participants from the ADNI-3 release (Weiner et al., 2017) included in this study.

### 2.1.1 Included Participants

Fifty cognitively normal (CN) older adults were included. Inclusion criteria required the availability of both advanced DWI protocol and high-resolution T1w MRI acquired on the same visit. Moreover, inclusion was restricted to participants with a multi-shell diffusion acquisition, which enables the use of multi-fiber and multi-tissue models, improving fiber orientation estimation and tractography accuracy (Jeurissen et al., 2014). In addition to imaging, we retained from the dataset the following imaging and cognitive measures: amyloid-β Centiloid (Aβ-Cent), which quantifies cortical amyloid load on a standardized scale where higher values indicate greater amyloid deposition (Iaccarino et al., 2025); white matter hyperintensity burden (WMHB), defined as WMH volume relative to total WM volume, with higher values indicating more extensive lesions (Wardlaw et al., 2013); and two global cognitive screening tests, the Mini Mental State Examination (MMSE) (Folstein et al., 1975) and the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005), both ranging from 0 to 30, with higher scores denoting

<footnote n="1">¹The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD).</footnote>
better cognitive functioning. All participants provided written informed consent, and the ADNI study was approved by the institutional review boards of all participating sites. Information for the included participants is reported in Table 1.

<table><caption>Table 1: Characteristics of the included participants.</caption><thead><tr><td>Variable</td><td>Value</td><td>Range</td><td>Unit</td></tr></thead><tbody><tr><td>Age</td><td>75.62 ± 7.28</td><td>65 - 92</td><td>years</td></tr><tr><td>Sex (F/M)</td><td>27/23</td><td>-</td><td>-</td></tr><tr><td>Education</td><td>16.76 ± 2.29</td><td>11 - 20</td><td>years</td></tr><tr><td>Aβ-Cent</td><td>21.42 ± 33.44</td><td>-22 - 108</td><td>-</td></tr><tr><td>WMHB</td><td>1.54 ± 3.10</td><td>0.01 - 19.57</td><td>%</td></tr><tr><td>MMSE</td><td>29.04 ± 1.23</td><td>25 - 30</td><td>-</td></tr><tr><td>MoCA</td><td>25.88 ± 2.38</td><td>20 - 30</td><td>-</td></tr></tbody></table>

### 2.1.2 MRI Procedure

MRI scans were acquired across 11 acquisition sites using Siemens 3T Prisma and Prisma fit systems. The protocol included high-resolution T1w structural imaging and multishell DWI. Structural scans consisted of a sagittal T1w magnetization-prepared rapid gradient-echo (MPRAGE) sequence (TR = 2300 ms, TE = 2.98 ms, TI = 900 ms, flip angle = 9, voxel size = 1.0 × 1.0 × 1.0 mm). Diffusion images were acquired using a multiband-accelerated (factor 3) monopolar spin-echo EPI sequence (TR = 3400 ms, TE = 71 ms, flip angle = 90, voxel size = 2.0 × 2.0 × 2.0 mm). The protocol included three diffusion shells (b = 500, 1000, and 2000s/mm<fnref n="2" />) for a total of 112 gradients directions.

## 2.2 Image Preprocessing

All images were preprocessed using a combination of the Anima toolbox<fnref n="2" />, the FMRIB Software Library (FSL v6.0.7.17, Jenkinson et al., 2012), the Advanced Normalization Tools (ANTs v2.6.0.dev1-gb775a15, Avants et al., 2008) and MRtrix3 (v3.0.5, J.-D. Tournier et al., 2019) software packages.

### 2.2.1 Anatomical MRI preprocessing

T1w structural images were first corrected for intensity non-uniformities using the N4 bias field correction algorithm (Tustison et al., 2010), as implemented in ANTs. Brain extraction was subsequently performed

<footnote n="2">²https://github.com/Inria-Empenn/Anima-Public</footnote>
using an atlas registration-based method implemented in Anima toolbox and the resulting skull-stripped
images were registered to DWI volumes using FSL's flirt. Non-linear registration to the brain-extracted
structural template space (MNI or MIITRA) was performed using the antsRegistration tool from
ANTs (Avants, Tustison, Song, et al., 2011), following a multi-stage procedure. The pipeline included
an initial rigid alignment (Rigid[0.1]) and an affine transformation (Affine[0.1]), both optimized
using mutual information (MI) as the similarity metric with 32 bins and a regular sampling of 25%.
Finally, a non-linear registration was carried out using the SyN transformation model (SyN[0.1, 3, 0])
with cross-correlation (CC, radius = 4) as the similarity metric. Each stage was iteratively optimized
with four resolution levels (-f 8x4x2x1) and Gaussian smoothing (-s 3x2x1x0vox). The convergence
criterion was defined as 10<sup>-7</sup> with up to 1000 iterations per level for the affine stages and 200 iterations
per level for the SyN stage. Forward and inverse deformation fields were generated and retained for
subsequent application in connectome construction.

2.2.2 Diffusion MRI preprocessing

Diffusion images underwent a comprehensive preprocessing pipeline designed to correct for acquisition-
related artifacts and improve signal quality. Initial correction for head motion and eddy current induced
distortions was performed using FSL's eddy tool. Susceptibility-induced distortions were addressed using
FSL's topup tool, based on fieldmaps acquired during the scanning session. These fieldmaps enabled
estimation of the off-resonance field and generation of spatial deformation fields to correct for geometric
distortions in the diffusion volumes. Subsequently, bias field correction was applied to remove residual low-
frequency intensity inhomogeneities using the dwibiascorrect function from MRtrix3, which internally
leverages the N4 algorithm implemented in ANTs (Tustison et al., 2010). This step improves intensity
homogeneity across the brain and supports more accurate modeling of white matter microstructure.
Denoising was carried out using the Rician-adapted Non-Local Means algorithm (Wiest-Daesslé et al.,
2008), implemented in the Anima toolbox. Finally, brain extraction was performed by applying the
brain mask derived from the preprocessed T1w structural image. All preprocessing steps were applied
identically across all experimental pipelines to ensure consistency and allow downstream methodological
variations to be isolated and systematically evaluated.

**2.3 Tractography Pipeline**

Whole-brain tractography was performed using MRtrix3. Tissue-specific response functions for WM, GM, and cerebrospinal fluid (CSF) were computed using the dwi2response dhollander algorithm
(Dhollander et al., 2016), and the corresponding Fiber orientation distributions (FODs) were calculated using dwi2fod msmt_csd (Jeurissen et al., 2014). All computations were performed within the brain mask and using a maximum spherical harmonic order of $l_{max} = 8$ for all compartments. Tractography was performed with tckgen using the iFOD2 probabilistic algorithm (J. D. Tournier et al., 2010), a maximum streamline length of 600 mm, and an angular threshold of 60 degrees. For pipelines incorporating ACT, streamlines were seeded from the gray matter–white matter interface, and anatomical tissues segmentation was provided via a five-tissue-type (5TT) image. A fixed number of 10 million streamlines were generated with dynamic seeding and backtracking enabled to improve coverage and anatomical plausibility. In contrast, for the pipeline excluding ACT, the brain mask was used as the seed region. Streamline weights were subsequently refined using SIFT2 (Smith et al., 2015), via the tcksift2 command, which adjusts each streamline's contribution based on the underlying FOD field. This step enhances the quantitative accuracy of structural connectivity estimates by aligning streamline counts more closely with local fiber density.

## 2.4 Connectome construction and Graph-theoretical analysis

Connectivity matrices were generated with tck2connectome from MRtrix3, which maps SIFT2-weighted streamlines to anatomical regions from a combined parcellation of the Schaefer 400-region cortical atlas (Schaefer et al., 2018) and the Tian 16-region (S1 scale) subcortical atlas (Tian et al., 2020). The resulting matrices were treated as weighted, undirected graphs: nodes correspond to 416 brain regions and edges encode anatomically informed connection strength (SIFT2-weighted streamline counts). For network level annotation, cortical nodes were assigned to the Yeo-7 functional networks (Thomas Yeo et al., 2011) and subcortical nodes retained their Tian labels.

A series of global and local network metrics were computed as described in Rubinov and Sporns, 2010 and using the NetworkX (v3.5) library in Python (v3.13.3). For path-based metrics, edge lengths were defined as the inverse of weights, so that stronger connections corresponded to shorter distances. Formal definitions of these measures are provided in the Supplementary Material (Section 1.1).

## 2.5 Experimental Conditions

To systematically assess the influence of processing choices on structural connectome reconstruction, a set of pipelines was designed varying two key factors: the structural template and the tissue segmentation strategy used for ACT. An overview of the structural connectome pipeline is shown in Figure 1.
Each subject's T1w image was nonlinearly registered to template space with antsRegistration (as explained in Section 2.2.1), using two population based templates: the widely used MNI template, specifically the MNI152NLin2009 verison (Fonov et al., 2009), and MIITRA, a template specifically designed for older adults (Ridwan et al., 2021). The inverse deformation fields were retained and used to bring template-based anatomical priors into subject space when needed.

Tissue segmentation for ACT was carried out using two alternative approaches. In one case, the FAST algorithm (Y. Zhang et al., 2001) was used to generate WM, GM, CSF probability maps directly from the T1w image. In the other case, the Atropos algorithm from ANTs was employed (Avants, Tustison, Wu, et al., 2011), which incorporates probabilistic tissue priors warped from template space into subject space. To evaluate the sensitivity of segmentation to prior information, Atropos was run with varying prior weights ranging from 0.25 to 0.5 in increments of 0.05, resulting to 6 different segmentations. Subcortical structures, including the thalamus, caudate, putamen, globus pallidus, hippocampus<fnref n="3" />, and amygdala, were segmented using the FSL tool run_first_all, which combines intensity profiles with Bayesian statistical shape and appearance models to delineate deep gray matter nuclei (Patenaude et al., 2011). In addition, a fifth tissue compartment was defined to include voxels not assigned to WM, GM, CSF, or subcortical structures. This compartment was extracted from the fifth volume of the tissue-type image generated via the 5ttgen function and typically includes regions such as lesions or unclassified tissue types. These regions and compartments were then integrated together to produce a 5TT image for use in ACT-based tractography.

These methodological choices resulted in 16 distinct pipelines, 2 templates × 8 variants. The eight variants comprise six configurations using ACT with ANTs Atropos at varying prior weights (collectively, ATR-ACT), one configuration using ACT with FSL FAST (FAST-ACT) and one configuration without ACT (NOACT), used as a reference. An overview of the pipelines can be found in Table 2. This experimental design allows for a systematic evaluation of how processing decisions, individually and in combination, affect the topology and the properties of derived structural connectomes. All other tractography procedures were held constant to isolate the specific impact of template selection and segmentation strategy.

## 2.6 Statistical Analysis

All statistical analyses were performed in Python (v3.12.2) using scipy (v1.13.1) and statsmodels (v0.14.2). For global network measures, paired comparisons between reference templates (MIITRA vs. MNI) were

<footnote n="3">³The hippocampus is a cortical structure, but we classified it as 'subcortical' following common brain atlas terminology.</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 1: Overview of the structural connectome reconstruction pipeline. T1w and DWI images undergo preprocessing and coregistration, followed by T1w segmentation and brain parcellation; whole-brain probabilistic tractography is then performed with or without ACT, and streamlines are aggregated into the structural connectome. The pipeline explicitly varies the template (MIITRA vs. MNI) and the segmentation strategy (ANTS Atropos vs. FSL FAST) to assess their impact on downstream connectome estimates.</figcaption></figure>
<table><caption>Table 2: Overview of structural connectome reconstruction pipelines varying template and segmentation strategy. For Atropos-based segmentations, $\delta$ indicates the prior probability weight applied during segmentation $\delta \in \{0.25, 0.30, 0.35, 0.40, 0.45, 0.50\}$.</caption><thead><tr><th>Pipeline ID</th><th>Template</th><th>Segmentation strategy</th><th>ACT</th></tr></thead><tbody><tr><td>MNI-NOACT</td><td>MNI</td><td>-</td><td>No</td></tr><tr><td>MIITRA-NOACT</td><td>MIITRA</td><td>-</td><td>No</td></tr><tr><td>MNI-FAST-ACT</td><td>MNI</td><td>FSL FAST</td><td>Yes</td></tr><tr><td>MIITRA-FAST-ACT</td><td>MIITRA</td><td>FSL FAST</td><td>Yes</td></tr><tr><td>MNI-ATR-δ-ACT</td><td>MNI</td><td>ANTs Atropos</td><td>Yes</td></tr><tr><td>MIITRA-ATR-δ-ACT</td><td>MIITRA</td><td>ANTs Atropos</td><td>Yes</td></tr></tbody></table>

conducted within each segmentation strategy. For each subject, the difference in metric values between templates was computed and normality of these paired differences was assessed using the Shapiro-Wilk test. When normality was satisfied, paired t-tests were applied; otherwise, the Wilcoxon signed-rank test was used. To account for multiple testing across all metrics and pipelines, p-values were corrected at the family level using the Benjamini-Hochberg false discovery rate (FDR) procedure. Corrected p-values (q-values) were used for visualization in boxplots, where significance was denoted by symbols corresponding to conventional thresholds ($^{†}q < 0.05$, $^{‡}q < 0.01$, $^{§}q < 0.001$).

Associations between global connectome metrics and cognitive or imaging measures were evaluated using linear mixed-effects models, with the measure, pipeline (template × segmentation), and their interaction used as fixed effects and subject as a random intercept. Four measures were examined: A$\beta$-Cent, WMHB, MMSE and MoCA scores. Age, sex, and total cerebral volume were included as covariates to control for demographic and anatomical variability. For each measure-metric pair, the omnibus interaction test (measure × pipeline) using a Wald $\chi^2$ statistic is reported, as well as the estimated slopes and their 95% confidence intervals within each pipeline. Pairwise comparisons of slopes across pipelines were corrected for multiple testing using FDR, and results were summarized both in tabular form and as heatmaps of corrected q-values. To complement these model-based results, Pearson correlation coefficients were also computed separately for each pipeline as descriptive measures of association.

For nodal network metrics, the effect of template choice was assessed while accounting for differences in segmentation strategies. For each node and technique, a mixed-effects model was fitted with template, technique, and their interaction as fixed effects, including age, sex, and total cerebral volume as covariates, and subject as a random intercept. For each node-technique pair, we estimated the effect of template (MIITRA vs. MNI) and assessed its significance. The p-values across nodes were corrected using the Benjamini-Hochberg FDR procedure within each metric. Significant effects were visualized in heatmaps where nodes were ordered according to the Yeo-7 network partition, allowing network-level summaries of
template related biases. Additionally, the percentage of nodes showing significant differences in template effect between pipelines was quantified, providing an estimate of cross pipeline variability.

# 3 Results

## 3.1 WM Segmentation evaluation between segmentation strategies

Figure 2 shows the comparison between WM segmentation obtained with FSL FAST and ANTs Atropos in three representative subjects. FSL FAST systematically failed to classify WMH as WM, leading to an underestimation of the true WM mask. This effect is particularly obvious in the periventricular areas, where WMH are more likely to occur and were consistently excluded from the segmented WM, thus generating false negatives (see the first line of Figure 2). Conversely, ANTs Atropos showed a more consistent inclusion of WMH within the WM segmentation. The performance depended on the choice of the prior weighting factor $\delta$. Lower values of $\delta$ ($\delta = 0.25$ or $0.30$) resulted in misclassification of WMH voxels as GM or CSF. Increasing the prior weight improved the correct labeling of WMH as WM, reducing segmentation errors, as for $\delta = 0.45$ or $\delta = 0.50$. However, higher $\delta$ values introduced a trade-off: while WMH were more accurately classified as WM, subcortical regions showed reduced anatomical detail due to the stronger influence of the priors. This effect was consistently observed across all examined subjects.

Overall, these results indicate that ANTs Atropos provides superior WM segmentation performance in the presence of WMH compared to FSL FAST. However, the optimal prior weight requires balancing accurate WMH inclusion with preservation of subcortical structural resolution.

## 3.2 Global Network Properties

In the ATR-ACT configurations, the MIITRA template shows higher sparsity, mean connectivity and global efficiency than MNI, as displayed in Figure 3. The magnitude of this effect increases as the anatomical prior weight of ANTs Atropos increases. In the FAST-ACT pipelines, template related differences are smaller and less consistent across the three metrics. In the NOACT conditions, a difference is evident for global efficiency and limited for sparsity and mean connectivity. Results for path length and higher order descriptors are provided in the Supplementary (Figure S1 and Table S1). Global network properties indicate that both template and segmentation systematically affect network density, average weight, and integration.
<figure><img src="image_5.png" /></figure>

Figure 2: Results of white matter segmentation for three representative subjects with different WMH area extensions: WMHB=7.22%, WMHB=2.99% and WMHB=0.01%. The first column shows the original T1-weighted images with overlaid WMH contours (red). The middle column displays the output of FSL FAST. The right panel illustrates ANTs Atropos segmentations obtained with different prior weighting factors ($\delta = 0.25-0.50$) and using MIITRA template priors registered in subject space.

## 3.3 Pipeline-Dependent Associations

Figure 4a shows that, within each template, sparsity and mean connectivity increase with respect to WMHB across all ACT-based pipelines, with visually similar slopes among all ATR-ACT configurations and comparable $R^2$ across different priors weights. FAST-ACT pipelines depart from this pattern; their fitted lines are visibly shifted relative to the ATR-ACT pipelines and its $R^2$ differs, indicating a change in both the strength and the tightness of the WMHB association. NOACT conditions exhibit flatter fits and smaller $R^2$, consistent with reduced sensitivity to WMHB in the absence of anatomical constraints. These qualitative differences in both slope and $R^2$ are present under MIITRA and under MNI, with the ATR-ACT pipelines remaining tightly clustered in each template panel.

Figure 4b confirms which of these visual differences are statistically reliable. Pairwise contrasts are concentrated between NOACT and many of ACT pipelines (consistent with NOACT's flatter slopes and lower $R^2$), and between FAST-ACT and the ATR-ACT pipelines (consistent with the displacement of FAST-ACT's lines and its higher $R^2$). Within the Atropos pipelines, significant contrasts are sparse across prior weights, in line with the near-parallel fitted lines and similar $R^2$ seen in Figure 4a. The same pattern holds for both metrics and under both templates.

The omnibus interaction tests (Table 3) indicate that the linear associations between global network
<figure><img src="image_6.png" /><figcaption>Figure 3: Boxplots for global connectome metrics across pipelines and registration templates. Top: sparsity (SP), middle: mean connectivity (MC), bottom: global efficiency (GE). Symbols denote significance levels from paired *t*-tests between templates within each pipeline ($^{†}q < 0.05$, $^{‡}q < 0.01$, $^{§}q < 0.001$).)</figcaption></figure>
properties and cognitive or imaging measures depend on the processing pipeline. WMHB shows consistent interactions across all global descriptors. MMSE and Aβ-Cent also display broad pipeline-dependent effects, whereas MoCA exhibits a more selective pattern, limited mainly to triadic measures. Overall, these results indicate that the detection and strength of associations between global network metrics and clinical or imaging measures are strongly conditioned by the connectome reconstruction pipeline, with ATR-ACT yielding coherent effects across priors, FAST-ACT shifting and amplifying them, and NOACT largely attenuating them. Supplementary scatter plots and heatmaps are provided in Fig. S2-S5, and the Wald χ² summaries in Tab. S2-S5.

<table><caption>Table 3: Omnibus tests (measure × pipeline) for global network metrics. Values are FDR-corrected q-values with significance levels denoted (†q < 0.05, ‡q < 0.01, §q < 0.001). SP: sparsity, MC: mean connectivity, GE: global efficiency, ASP: average shortest path, ACL: average clustering, AS: assortativity, TR: transitivity.</caption><thead><tr><th rowspan="2">Metric</th><th colspan="4">Measure</th></tr><tr><th>MMSE</th><th>MoCA</th><th>Aβ-Cent</th><th>WMHB</th></tr></thead><tbody><tr><td>SP</td><td>≈0§</td><td>6.76·10<sup>-2</sup></td><td>≈0§</td><td>≈0§</td></tr><tr><td>MC</td><td>≈0§</td><td>7.39·10<sup>-1</sup></td><td>≈0§</td><td>≈0§</td></tr><tr><td>GE</td><td>3.07·10<sup>-7§</sup></td><td>6.93·10<sup>-1</sup></td><td>2.01·10<sup>-5§</sup></td><td>≈0§</td></tr><tr><td>ASP</td><td>9.53·10<sup>-5§</sup></td><td>7.82·10<sup>-1</sup></td><td>6.03·10<sup>-8§</sup></td><td>≈0§</td></tr><tr><td>ACL</td><td>1.33·10<sup>-8§</sup></td><td>1.02·10<sup>-4§</sup></td><td>1.30·10<sup>-3‡</sup></td><td>≈0§</td></tr><tr><td>AS</td><td>≈0§</td><td>1</td><td>≈0§</td><td>≈0§</td></tr><tr><td>TR</td><td>≈0§</td><td>8.87·10<sup>-3‡</sup></td><td>≈0§</td><td>≈0§</td></tr></tbody></table>

## 3.4 Template effect on Nodal Network Properties

Figure 5 summarizes node-wise template related differences by pipeline and network. For strength, all pipelines show numerous nodes with significant template related differences across cortical and subcortical networks. For local efficiency, ATR-ACT exhibits widespread significant differences, whereas FAST-ACT and NOACT yield very few significant nodes. For clustering, significant differences occur mainly in subcortical nodes using ATR-ACT and NOACT, with fewer cortical nodes reaching significance; FAST-ACT shows fewer significant nodes overall. For betweenness centrality, significant node-wise differences are sparse in all pipelines, with most effects confined to subcortical regions in ATR-ACT, more widespread effects in NOACT, and very few significant nodes in FAST-ACT. In ATR-ACT, the general magnitude of these node-wise effects increases with the Atropos prior weight. Overall, node-level template effects are strong and spatially extensive in ATR-ACT, whereas FAST-ACT and NOACT produce only sparse significant differences. Further nodal results are provided in the Supplementary (Figure S6).
<figure><img src="image_7.png" /><figcaption>Figure 4: a) Scatter plots for two global metrics (left: sparsity (SP); right: mean connectivity (MC)) shown separately by template (top: MIITRA; bottom: MNI) where each point is a subject (x-axis: WMHB; y-axis: metric). Dashed lines indicate per-pipeline linear fits and legends report $R^2$ for each fit.
b) Lower-triangular heatmaps of FDR-corrected *q*-values (Benjamini-Hochberg) for pairwise differences between the slopes estimated in (a). Cells are annotated with significance markers ($^{†}q < 0.05$, $^{‡}q < 0.01$, $^{§}q < 0.001$).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 5: **a)** Heatmaps of the MIITRA–MNI median node effect for four nodal metrics (from top to bottom: strength (ST), local efficiency (LE), clustering (CLC), betweenness centrality (BC)) across pipelines. Nodes are ordered and color-coded by Yeo-7 networks plus the subcortical network (legend at the bottom). Warm/cool colors indicate positive/negative MIITRA–MNI differences. **b)** For each of the four metrics shown in panel a, bar plots report the percentage of nodes with a significant MIITRA–MNI effect ($q < 0.001$) across pipelines and in Yeo-7 networks.</figcaption></figure>
# 4 Discussion

Reliable structural connectome reconstruction in aging brains requires accurately handling specific anatomical challenges, such as WMH and atrophy, which are often overlooked by standard pipelines. To address this, we investigated the impact of two fundamental processing choices: the tissue segmentation strategy and the reference template within an ACT framework. Beyond quantifying differences in graph metrics, we sought to determine how these processing decisions interact to shape the topology of the reconstructed networks and whether they modulate the detection of biological associations with markers of pathology and cognitive function.

## 4.1 Impact of Segmentation and Template Choice on Global Network Properties

When examining global network properties, we observed that sparsity values remained relatively stable across the Atropos-based segmentations, irrespective of the specific weighting applied to the tissues priors (Figure 3). This suggests that, within the Atropos framework, the overall density of the connectomes is only minimally affected by the exact prior weighting, and that the choice of template introduces only modest differences in density. Subtle shifts were nevertheless evident, with MIITRA-derived connectomes often displaying slightly higher sparsity values. This pattern reflects a lower proportion of weak connections being retained when the MIITRA template is used, pointing to small but systematic influences of template choice even when priors weight remain constant (Figure 3). By contrast, FAST-ACT and NOACT pipelines produced significantly different sparsity profiles that were clearly distinct from those obtained with Atropos. In particular, the NOACT condition yielded substantially lower sparsity, consistent with the generation of denser connectomes. This result aligns with expectations, as the absence of anatomical priors in tractography relaxes biological constraints and allows the reconstruction of spurious streamlines, leading to artificially inflated connectivity matrices. Unconstrained tracking is known to inflate false positives (Maier-Hein et al., 2017; Smith et al., 2012), while anatomical priors reduce such biases. The FAST-ACT approach, while closer to ATR-ACT in terms of average sparsity, exhibited greater variability, likely reflecting differences in how tissue classes are delineated during segmentation. Taken together, these findings suggest that segmentation strategy exerts a stronger influence on sparsity than template choice, with Atropos-based segmentations yielding stable, moderately sparse connectomes, whereas FAST-ACT and especially NOACT pipelines introduce substantial deviations in network density. In the context of healthy aging, where the effective differences in connectivity density are expected to be minimal, this observation highlights the importance of relying on anatomically informed priors to
avoid methodological biases that could obscure or exaggerate biological effects. Beyond sparsity, we also identified significant differences in mean connectivity between the two registration templates. Since this connectivity metric represents the average strength of connections across the entire brain, these findings indicate that the choice of template affects how reliably streamlines are assigned to the parcellated regions, thus shaping the overall density of the connectome. In other words, the anatomical alignment and tissues priors embedded in each template influence the reconstruction of the network in a systematic manner, thereby altering global connectivity measures. This reinforces the notion that template selection is not a neutral preprocessing decision but a methodological choice with direct consequences for downstream graph-theoretical analyses. As such, researchers should account for potential template effects when comparing results across studies or designing analytic pipelines, as distinct templates may yield baseline differences in global connectivity profiles with potential implications for both statistical outcomes and biological interpretation. Differences between MIITRA and MNI were particularly evident in measures of network integration. Connectomes registered to the MIITRA template exhibited higher global efficiency compared to those based on the MNI template. Nevertheless, it is worth noting that global efficiency scale with network density, template and segmentation-driven changes in sparsity should be accounted for when interpreting integration. Since global efficiency quantifies the capacity of the network to exchange information effectively, this pattern points to a more integrated network organization under MIITRA. These results are especially noteworthy given the age range of the studied cohort (65–92 years). Normal aging is typically associated with reductions in efficiency, reflecting a decline in network integration (Li et al., 2020; Madden et al., 2020). The fact that MIITRA produces connectomes with comparatively higher efficiency may indicate that its age-specific anatomical priors better preserve or emphasize long-range connections, which are particularly vulnerable to registration biases (Fillmore et al., 2015). By contrast, the use of MNI, a template not optimized for older populations, may introduce misalignments that artificially dampen integration metrics, thereby exaggerating the appearance of age-related decline.

Overall, these findings underscore that both segmentation strategy and template choice significantly shape global properties of the structural connectome, albeit in different ways. Segmentation decisions, particularly the inclusion or exclusion of anatomically constrained priors, primarily influence network density, while template choice systematically biases integration metrics. For studies of aging, where detecting subtle alterations in global network properties is of central importance, the adoption of age-appropriate templates such as MIITRA may provide a more accurate representation of structural connectivity, whereas more generic templates may risk underestimating integration and thereby mischaracterizing age-related or pathology-related changes.
### 4.2 Methodological Biases in the Relationship Between Clinical Factors and Network properties

The omnibus interaction tests for global metrics showed that the associations between network properties and each cognitive or imaging measure are pipeline dependent. Across descriptors, FAST-ACT and NOACT systematically bias these relationships in opposite directions: FAST-ACT generally yields steeper slopes and higher $R^2$ values, giving the impression of stronger measure-network associations, whereas NOACT leads to flatter slopes and lower $R^2$, thereby weakening or obscuring effects. ATR-ACT pipelines lie in between these two extremes, with comparable effect sizes and reproducible estimates across prior weights and templates.

Within this general pattern, WMHB shows the most robust and widespread interactions, especially with sparsity and mean connectivity. In line with established notions that WMHB disrupt long-range WM pathways, higher lesion load was associated with sparser connectomes. Importantly, however, the strength and expression of this association were highly dependent on the chosen segmentation strategy, underscoring the methodological sensitivity of connectome reconstruction in the presence of pathology. FAST-ACT pipelines showed the steepest associations, yielding networks with both significantly increased sparsity and inflated mean connectivity in individuals with greater WMHB. This apparently paradoxical pattern can be attributed to segmentation bias: FAST tends to misclassify WMH regions as gray matter, thereby preventing tractography from reconstructing fibers that traverse lesion sites (Figure 2). As a result, many potential connections are excluded, producing networks that are simultaneously “weaker,” due to reduced density, and “stronger,” due to the selective retention of high weight streamlines. Such biases risk distorting the interpretation of how vascular lesions impact network topology, as the measured associations may reflect segmentation artifacts rather than true biological effects. Studies on WMH consistently show that lesion voxels with atypical intensities relate to partially preserved WM (Min et al., 2021; Svärd et al., 2017; Wardlaw et al., 2015). For this reason ACT pipelines must handle WMH explicitly (Theaud et al., 2018). In contrast, NOACT pipelines yielded artificially dense connectomes that were largely insensitive to WMHB. In the absence of anatomical priors, tractography is free to generate spurious streamlines, effectively masking the true impact of pathology on network density and connectivity strength. While this approach reduces the biases associated with FAST based WM segmentations, it introduces its own form of distortion by minimizing lesion-related effects. ATR-ACT pipelines occupied an intermediate and more biologically plausible position. They preserved the expected negative association between WMHB and network sparsity, while avoiding the exaggeration introduced by FAST-ACT and the artificial insensitivity of NOACT. This suggests that Atropos segmentation may provide more balanced reconstruction strategy in aging cohorts, where WMH are common and potentially
confounding. By more robustly handling lesion regions, Atropos allows for a more faithful characterization of how vascular pathology shapes structural connectivity, thus reducing the risk of methodological bias in graph theoretical analyses.

Consistent, though generally weaker, associations also emerged for global cognition and amyloid burden. MMSE and Aβ-Cent show broad pipeline-dependent effects across several global properties, with FAST-ACT and NOACT again tending to either overestimate or dampen associations relative to the more stable ATR-ACT pipelines. MoCA, in contrast, showed fewer and less consistent interactions, mostly confined to a subset of higher-order descriptors. Given the limited variability and redundancy between MoCA and MMSE scores in this cognitively normal cohort, these associations are modest and should be interpreted with caution.

Taken together, these results indicate that the apparent impact of WMHB and other clinical factors on global network properties cannot be interpreted without explicit consideration of the connectome reconstruction pipeline. Methodological choices not only modulate the strength of observed associations but may also qualitatively alter their direction and meaning. In studies of aging populations, rigorous control of connectome reconstruction choices is therefore essential to ensure that observed network alterations reflect biological rather than methodological effects.

## 4.3 The Influence of Prior Weighting on Nodal Network Metrics

Atropos method explicitly modulates the influence of spatial priors on tissue classification, with higher prior weights enforcing atlas driven labels. When considering nodal-level graph measures, our results reveal a clear interaction between template choice and the weighting of tissues priors in Atropos segmentation. Under conditions of strong prior influence ($\delta = 0.5$), we observed widespread and significant differences across multiple nodal metrics spanning a broad range of brain regions. This finding indicates that, when segmentation is heavily constrained by priors, the template exerts a strong influence on the local topology of the reconstructed connectome. In this context, template specific anatomical features are amplified, shaping the distribution of streamlines and systematically biasing nodal measures. When an intermediate prior weighting was used ($\delta = 0.35$), the extent of template-driven variability was reduced. Differences persisted for almost every metric, but the overall number and spatial distribution of affected nodes was smaller. This suggests that a more balanced weighting scheme allows subject specific anatomical data to exert a greater influence during segmentation, thereby mitigating template driven biases. At the lowest prior weighting ($\delta = 0.25$), the impact of template choice was considerably reduced. Only residual differences were detectable, indicating that nodal metrics in this setting were
largely robust to the choice of registration template. From a methodological standpoint, these results highlight that the interaction between segmentation strategy and template selection is not uniform but critically dependent on the degree to which prior information constrains the segmentation process. High reliance on priors increases sensitivity to template features, while lower reliance shifts the balance toward individual anatomical variation, thereby reducing template-related biases.

Practically, this has important implications for studies aiming at reproducibility and cross-cohort comparability. If high prior weights are used, nodal-level graph measures may be strongly contingent on the specific template employed, limiting the generalizability of findings across pipelines or populations. In contrast, intermediate or lower prior weights appear to yield connectomes that are more stable and less sensitive to template choice, providing a more reliable basis for investigating local network topology. Given nodal sensitivity to both tissue priors and template definitions, reporting prior weights and atlas definitions is essential.

## 4.4 Limitations and Perspectives

This study has some limitations that should be acknowledged. First, the absence of a ground truth connectome limits the ability to determine which segmentation strategy or template provides the most accurate representation of structural connectivity. Our conclusions are therefore based on relative comparisons between pipelines, rather than on an absolute benchmark of biological validity. Second, our analyses were restricted to healthy older adults. While this cohort is highly relevant for studying age-related changes in network organization and the impact of WMH, the generalizability of our findings to younger populations or to clinical groups with different neuropathological profiles remains uncertain. Despite these limitations, the present work provides important perspectives for future research. The systematic evaluation of segmentation and template choices highlights methodological factors that can deeply shape both global and nodal network properties. Building on these findings, future studies should incorporate test-retest datasets and multi cohort comparisons to establish the reproducibility of connectome metrics under different preprocessing strategies. Integrating lesion-aware or lesion-filling approaches could further improve the robustness of segmentation in aging and clinical populations with high WMHB. Moreover, extending the analysis to patients with neurodegenerative or cerebrovascular conditions will be essential to determine whether the methodological sensitivities observed here generalize to pathological brains, or whether additional adaptations are needed. Finally, the development and validation of harmonization strategies that explicitly account for template and segmentation effects may help ensure comparability across studies, thereby facilitating multi site investigations of structural connectivity in aging.
# 5 Conclusion

This study presents a systematic evaluation of structural connectome pipelines in cognitively normal older adults, isolating the effects of reference template selection and tissue segmentation while holding tractography and parcellation constant. This work provides a principled framework for understanding how preprocessing choices propagate into downstream network measures and clinical connectome analyses, and it offers concrete guidance on making and reporting these choices in aging cohorts. The findings demonstrate that what one concludes from connectomes is strongly shaped by the preprocessing choices made before tractography and graph construction. Adopting transparent, standardized reporting of these decisions is therefore essential for comparability across sites and studies. Future efforts should extend this benchmarking to pathological populations and develop harmonization strategies that explicitly model template and segmentation effects. By embedding these considerations into study design and reporting, researchers can improve the interpretability, reproducibility, and translational value of structural connectomics in aging.
# Data and Code Availability

The dataset used in this study is available at https://ida.loni.usc.edu and described in Weiner et al., 2017. The code developed is openly accessible at https://github.com/CarloFerritto/Structural_connectomes_in_Aging.

# Author Contributions

C.F. curated the data, developed the code, performed the formal analysis, produced the visualizations, and wrote the original draft. J.C. and G.L. contributed to the analysis and writing (review and editing), provided supervision and validation. P.-Y.J. provided clinical validation and contributed to writing (review and editing). All authors approved the final manuscript.

# Funding

This work was funded by the French National Research Agency (Agence Nationale de la Recherche, ANR) under grant ANR-22-CE45-0011 (Project identifier: NODAL, PI: J. Coloigner).

# Declaration of Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

# Acknowledgements

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech;
BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; Eurolmmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).

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# Supplementary Material

## 1 Methods

### 1.1 Graph-theoretical metrics

Connectivity matrices were treated as weighted, undirected graphs $G = (V, E)$, where nodes $V$ corresponded to brain regions and edges $E$ to streamline-based connections with weights $w_{ij} > 0$ (streamline counts). All shortest-path-based metrics were computed using $1/w_{ij}$ as edge lengths.

#### 1.1.1 Global metrics

* **Sparsity (SP):** the proportion of absent connections in the matrix:

$$SP = 1 - \frac{2|E|}{N(N-1)},$$

where $|E|$ is the number of non-zero entries in the connectivity matrix.

* **Mean connectivity (MC):** the average weight of all non-zero edges:

$$MC = \frac{1}{|E|} \sum_{(i,j) \in E} w_{ij}.$$

* **Global efficiency (GE):** the average inverse shortest path length between all pairs of nodes:

$$GE = \frac{1}{N(N-1)} \sum_{i \neq j} \frac{1}{d_{ij}},$$

where $d_{ij}$ is the shortest path length between nodes $i$ and $j$ computed using $1/w_{ij}$ as edge lengths.

* **Average shortest path (ASP):** the mean shortest distance between all node pairs:

$$ASP = \frac{1}{N(N-1)} \sum_{i \neq j} d_{ij}.$$
* **Transitivity (TR):** a measure of global clustering, computed as:

$$TR = \frac{3 \times \text{number of triangles}}{\text{number of connected triples}}$$

* **Assortativity (AS):** the Pearson correlation coefficient between the degrees of connected nodes, indicating the tendency of nodes to connect with others of similar degree.

* **Average clustering (ACL):** the mean of the local clustering coefficients (CLC), incorporating edge weights.

### 1.1.2 Local metrics

* **Degree (DE):** number of directly connected neighbors:

$$DE_i = \sum_j \delta(w_{ij} > 0).$$

* **Strength (ST):** total weight of edges connected to a node:

$$ST_i = \sum_j w_{ij}.$$

* **Local efficiency (LE):** efficiency of information transfer in a node's local neighborhood:

$$LE_i = \frac{1}{k_i(k_i - 1)} \sum_{\substack{j,k \in \mathcal{N}(i) \\ j \neq k}} \frac{1}{d_{jk}},$$

where $\mathcal{N}(i)$ is the set of neighbors of node i and $d_{jk}$ are shortest path lengths computed with inverse weights.

* **Clustering coefficient (CLC):** the fraction of closed triplets around a node, weighted by the geometric mean of edge weights.

* **Betweenness centrality (CB):** the fraction of all shortest paths that pass through a node:

$$BC_i = \sum_{s \neq i \neq t} \frac{\sigma_{st}(i)}{\sigma_{st}},$$
where $\sigma_{st}$ is the number of shortest paths from node s to node t, and $\sigma_{st}(i)$ is the number of those paths that pass through node i.

* Closeness centrality (CC): the reciprocal of the average shortest distance from a node to all others:

$$CC_i = \frac{N - 1}{\sum_{j \neq i} d_{ij}}$$

## 2 Results

<figure><img src="image_9.png" /><figcaption>Figure S1: Comparison of global connectome metrics across pipelines and registration templates. Top-left: Average shortest path (ASP), Top-right: Assortativity (AS), Bottom-left: Average clustering (ACL), Bottom-right: transitivity (TR). Symbols denote significance levels from paired t-tests between templates within each pipeline (†q < 0.05, ‡q < 0.01, §q < 0.001).)</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Figure S2: **a)** Scatter plots for five global metrics (in top to bottom order: Global efficiency (GE), Average shortest path (ASP), Average clustering (ACL), Assortativity (AS), Transitivity (TR) shown separately by template (left: MIITRA; right: MNI) where each point is a subject (x-axis: white matter hyperintensity burden (WMHB); y-axis: metric). Dashed lines indicate per-pipeline linear fits and legends report $R^2$ for each fit. **b)** Lower-triangular heatmaps of FDR-corrected *q*-values (Benjamini-Hochberg) for pairwise differences between the slopes estimated in (a). Cells are annotated with significance markers (†*q* < 0.05, ‡*q* < 0.01, §*q* < 0.001).</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure S3: **a)** Scatter plots for seven global metrics (in top to bottom order: Sparsity (SP), Mean connectivity (MC), Global efficiency (GE), Average shortest path (ASP), Average clustering (ACL), Asortativity (AS), Transitivity (TR) shown separately by template (left: MIITRA; right: MNI) where each point is a subject (x-axis: MMSE Score; y-axis: metric). Dashed lines indicate per-pipeline linear fits and legends report *R*² for each fit. **b)** Lower-triangular heatmaps of FDR-corrected *q*-values (Benjamini-Hochberg) for pairwise differences between the slopes estimated in (a). Cells are annotated with significance markers (†*q* < 0.05, ‡*q* < 0.01, §*q* < 0.001).</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Figure S4: **a)** Scatter plots for seven global metrics (in top to bottom order: Sparsity (SP), Mean connectivity (MC), Global efficiency (GE), Average shortest path (ASP), Average clustering (ACL), Asortativity (AS), Transitivity (TR) shown separately by template (left: MIITRA; right: MNI) where each point is a subject (x-axis: MoCA Score; y-axis: metric). Dashed lines indicate per-pipeline linear fits and legends report *R*² for each fit. **b)** Lower-triangular heatmaps of FDR-corrected *q*-values (Benjamini-Hochberg) for pairwise differences between the slopes estimated in (a). Cells are annotated with significance markers (†*q* < 0.05, ‡*q* < 0.01, §*q* < 0.001).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figure S5: **a)** Scatter plots for seven global metrics (in top to bottom order: Sparsity (SP), Mean connectivity (MC), Global efficiency (GE), Average shortest path (ASP), Average clustering (ACL), Assortativity (AS), Transitivity (TR) shown separately by template (left: MIITRA; right: MNI) where each point is a subject (x-axis: Aβ-Centiloid; y-axis: metric). Dashed lines indicate per-pipeline linear fits and legends report $R^2$ for each fit. **b)** Lower-triangular heatmaps of FDR-corrected *q*-values (Benjamini-Hochberg) for pairwise differences between the slopes estimated in (a). Cells are annotated with significance markers (†*q* < 0.05, ‡*q* < 0.01, §*q* < 0.001).</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Figure S6: **a)** Heatmaps of the MIITRA-MNI median node effect for four nodal metrics (from top to bottom: Degree (DE), closeness centrality (CC)) across pipelines. Nodes are ordered and color-coded by Yeo-7 networks plus the subcortical network (legend at the bottom). Warm/cool colors indicate positive/negative MIITRA-MNI differences. **b)** Bar plots report, for each pipeline and network, the percentage of nodes with a significant MIITRA-MNI effect (q < 0.001).</figcaption></figure>
<table><caption>Table S1: Mean template effect per metric and pipeline with 95% CI and FDR-corrected *q*-values. Significance levels: † *q* < 0.05, ‡ *q* < 0.01, § *q* < 0.001. SP: sparsity, MC: mean connectivity, GE: global efficiency, ASP: average shortest path, ACL: average clustering, AS: assortativity, TR: transitivity.</caption><thead><tr><th>Pipeline</th><th>Metric</th><th>Δ (MIITRA-MNI)</th><th>95% CI</th><th>q-value</th></tr></thead><tbody><tr><td rowspan="7">ATR-25-ACT</td><td>SP</td><td>1.46 · 10<sup>-2</sup></td><td>[1.17 · 10<sup>-2</sup>, 1.75 · 10<sup>-2</sup>]</td><td>1.31 · 10<sup>-10</sup>§</td></tr><tr><td>MC</td><td>4.48</td><td>[3.69, 5.27]</td><td>1.09 · 10<sup>-14</sup>§</td></tr><tr><td>GE</td><td>1.58 · 10<sup>1</sup></td><td>[1.25 · 10<sup>1</sup>, 1.92 · 10<sup>1</sup>]</td><td>2.49 · 10<sup>-12</sup>§</td></tr><tr><td>ASP</td><td>-6.66 · 10<sup>-5</sup></td><td>[-8.74 · 10<sup>-5</sup>, -4.59 · 10<sup>-5</sup>]</td><td>2.02 · 10<sup>-8</sup>§</td></tr><tr><td>ACL</td><td>9.84 · 10<sup>-6</sup></td><td>[-9.09 · 10<sup>-6</sup>, 2.88 · 10<sup>-5</sup>]</td><td>3.24 · 10<sup>-1</sup></td></tr><tr><td>AS</td><td>9.78 · 10<sup>-3</sup></td><td>[7.20 · 10<sup>-3</sup>, 1.24 · 10<sup>-2</sup>]</td><td>1.29 · 10<sup>-9</sup>§</td></tr><tr><td>TR</td><td>-7.09 · 10<sup>-3</sup></td><td>[-8.40 · 10<sup>-3</sup>, -5.78 · 10<sup>-3</sup>]</td><td>4.19 · 10<sup>-14</sup>§</td></tr><tr><td rowspan="7">ATR-30-ACT</td><td>SP</td><td>1.30 · 10<sup>-2</sup></td><td>[1.08 · 10<sup>-2</sup>, 1.52 · 10<sup>-2</sup>]</td><td>3.62 · 10<sup>-15</sup>§</td></tr><tr><td>MC</td><td>4.31</td><td>[3.68, 4.94]</td><td>4.06 · 10<sup>-17</sup>§</td></tr><tr><td>GE</td><td>1.94 · 10<sup>1</sup></td><td>[1.56 · 10<sup>1</sup>, 2.31 · 10<sup>1</sup>]</td><td>1.66 · 10<sup>-13</sup>§</td></tr><tr><td>ASP</td><td>-8.37 · 10<sup>-5</sup></td><td>[-1.07 · 10<sup>-4</sup>, -6.07 · 10<sup>-5</sup>]</td><td>9.56 · 10<sup>-9</sup>§</td></tr><tr><td>ACL</td><td>-1.05 · 10<sup>-6</sup></td><td>[-2.04 · 10<sup>-5</sup>, 1.83 · 10<sup>-5</sup>]</td><td>9.30 · 10<sup>-1</sup></td></tr><tr><td>AS</td><td>9.14 · 10<sup>-3</sup></td><td>[7.14 · 10<sup>-3</sup>, 1.11 · 10<sup>-2</sup>]</td><td>7.26 · 10<sup>-12</sup>§</td></tr><tr><td>TR</td><td>-6.40 · 10<sup>-3</sup></td><td>[-7.57 · 10<sup>-3</sup>, -5.22 · 10<sup>-3</sup>]</td><td>4.12 · 10<sup>-14</sup>§</td></tr><tr><td rowspan="7">ATR-35-ACT</td><td>SP</td><td>1.21 · 10<sup>-2</sup></td><td>[9.83 · 10<sup>-3</sup>, 1.44 · 10<sup>-2</sup>]</td><td>6.59 · 10<sup>-14</sup>§</td></tr><tr><td>MC</td><td>4.26</td><td>[3.60, 4.93]</td><td>1.95 · 10<sup>-16</sup>§</td></tr><tr><td>GE</td><td>2.33 · 10<sup>1</sup></td><td>[1.90 · 10<sup>1</sup>, 2.77 · 10<sup>1</sup>]</td><td>6.59 · 10<sup>-14</sup>§</td></tr><tr><td>ASP</td><td>-1.05 · 10<sup>-4</sup></td><td>[-1.31 · 10<sup>-4</sup>, -7.84 · 10<sup>-5</sup>]</td><td>3.42 · 10<sup>-10</sup>§</td></tr><tr><td>ACL</td><td>-1.02 · 10<sup>-5</sup></td><td>[-3.19 · 10<sup>-5</sup>, 1.15 · 10<sup>-5</sup>]</td><td>3.70 · 10<sup>-1</sup></td></tr><tr><td>AS</td><td>8.69 · 10<sup>-3</sup></td><td>[6.85 · 10<sup>-3</sup>, 1.05 · 10<sup>-2</sup>]</td><td>2.48 · 10<sup>-12</sup>§</td></tr><tr><td>TR</td><td>-6.04 · 10<sup>-3</sup></td><td>[-7.30 · 10<sup>-3</sup>, -4.78 · 10<sup>-3</sup>]</td><td>1.76 · 10<sup>-12</sup>§</td></tr><tr><td rowspan="7">ATR-40-ACT</td><td>SP</td><td>1.25 · 10<sup>-2</sup></td><td>[1.01 · 10<sup>-2</sup>, 1.49 · 10<sup>-2</sup>]</td><td>1.05 · 10<sup>-13</sup>§</td></tr><tr><td>MC</td><td>4.59</td><td>[3.84, 5.34]</td><td>9.94 · 10<sup>-16</sup>§</td></tr><tr><td>GE</td><td>2.67 · 10<sup>1</sup></td><td>[2.20 · 10<sup>1</sup>, 3.14 · 10<sup>1</sup>]</td><td>9.18 · 10<sup>-15</sup>§</td></tr><tr><td>ASP</td><td>-1.20 · 10<sup>-4</sup></td><td>[-1.51 · 10<sup>-4</sup>, -8.97 · 10<sup>-5</sup>]</td><td>4.30 · 10<sup>-10</sup>§</td></tr><tr><td>ACL</td><td>-2.73 · 10<sup>-5</sup></td><td>[-5.01 · 10<sup>-5</sup>, -4.50 · 10<sup>-6</sup>]</td><td>2.42 · 10<sup>-2</sup>†</td></tr><tr><td>AS</td><td>7.54 · 10<sup>-3</sup></td><td>[5.66 · 10<sup>-3</sup>, 9.42 · 10<sup>-3</sup>]</td><td>2.98 · 10<sup>-10</sup>§</td></tr><tr><td>TR</td><td>-7.16 · 10<sup>-3</sup></td><td>[-8.46 · 10<sup>-3</sup>, -5.86 · 10<sup>-3</sup>]</td><td>2.90 · 10<sup>-14</sup>§</td></tr><tr><td rowspan="7">ATR-45-ACT</td><td>SP</td><td>1.14 · 10<sup>-2</sup></td><td>[9.06 · 10<sup>-3</sup>, 1.38 · 10<sup>-2</sup>]</td><td>1.32 · 10<sup>-12</sup>§</td></tr><tr><td>MC</td><td>4.48</td><td>[3.82, 5.15]</td><td>4.23 · 10<sup>-17</sup>§</td></tr><tr><td>GE</td><td>3.06 · 10<sup>1</sup></td><td>[2.59 · 10<sup>1</sup>, 3.52 · 10<sup>1</sup>]</td><td>1.03 · 10<sup>-16</sup>§</td></tr><tr><td>ASP</td><td>-1.41 · 10<sup>-4</sup></td><td>[-1.74 · 10<sup>-4</sup>, -1.09 · 10<sup>-4</sup>]</td><td>2.29 · 10<sup>-10</sup>§</td></tr><tr><td>ACL</td><td>-3.34 · 10<sup>-5</sup></td><td>[-5.87 · 10<sup>-5</sup>, -8.10 · 10<sup>-6</sup>]</td><td>1.33 · 10<sup>-2</sup>†</td></tr><tr><td>AS</td><td>6.72 · 10<sup>-3</sup></td><td>[4.90 · 10<sup>-3</sup>, 8.54 · 10<sup>-3</sup>]</td><td>2.62 · 10<sup>-9</sup>§</td></tr><tr><td>TR</td><td>-7.05 · 10<sup>-3</sup></td><td>[-8.41 · 10<sup>-3</sup>, -5.70 · 10<sup>-3</sup>]</td><td>1.39 · 10<sup>-13</sup>§</td></tr><tr><td rowspan="7">ATR-50-ACT</td><td>SP</td><td>1.06 · 10<sup>-2</sup></td><td>[8.07 · 10<sup>-3</sup>, 1.32 · 10<sup>-2</sup>]</td><td>1.10 · 10<sup>-10</sup>§</td></tr><tr><td>MC</td><td>4.52</td><td>[3.88, 5.16]</td><td>1.31 · 10<sup>-17</sup>§</td></tr><tr><td>GE</td><td>3.62 · 10<sup>1</sup></td><td>[3.17 · 10<sup>1</sup>, 4.07 · 10<sup>1</sup>]</td><td>2.26 · 10<sup>-19</sup>§</td></tr><tr><td>ASP</td><td>-1.77 · 10<sup>-4</sup></td><td>[-2.06 · 10<sup>-4</sup>, -1.48 · 10<sup>-4</sup>]</td><td>1.00 · 10<sup>-15</sup>§</td></tr><tr><td>ACL</td><td>-5.63 · 10<sup>-5</sup></td><td>[-8.31 · 10<sup>-5</sup>, -2.96 · 10<sup>-5</sup>]</td><td>3.86 · 10<sup>-5</sup>§</td></tr><tr><td>AS</td><td>5.51 · 10<sup>-3</sup></td><td>[3.67 · 10<sup>-3</sup>, 7.35 · 10<sup>-3</sup>]</td><td>3.27 · 10<sup>-7</sup>§</td></tr><tr><td>TR</td><td>-7.37 · 10<sup>-3</sup></td><td>[-8.91 · 10<sup>-3</sup>, -5.83 · 10<sup>-3</sup>]</td><td>1.76 · 10<sup>-12</sup>§</td></tr><tr><td rowspan="7">FAST-ACT</td><td>SP</td><td>-1.51 · 10<sup>-3</sup></td><td>[-2.19 · 10<sup>-3</sup>, -8.31 · 10<sup>-4</sup>]</td><td>6.47 · 10<sup>-5</sup>§</td></tr><tr><td>MC</td><td>-3.40 · 10<sup>-1</sup></td><td>[-5.49 · 10<sup>-1</sup>, -1.32 · 10<sup>-1</sup>]</td><td>2.42 · 10<sup>-3</sup>†</td></tr><tr><td>GE</td><td>-1.55</td><td>[-3.94, 8.33 · 10<sup>-1</sup>]</td><td>1.70 · 10<sup>-1</sup></td></tr><tr><td>ASP</td><td>1.50 · 10<sup>-5</sup></td><td>[-1.90 · 10<sup>-6</sup>, 3.19 · 10<sup>-5</sup>]</td><td>5.00 · 10<sup>-2</sup>†</td></tr><tr><td>ACL</td><td>2.97 · 10<sup>-5</sup></td><td>[1.20 · 10<sup>-5</sup>, 4.74 · 10<sup>-5</sup>]</td><td>9.77 · 10<sup>-5</sup>§</td></tr><tr><td>AS</td><td>2.32 · 10<sup>-3</sup></td><td>[1.17 · 10<sup>-3</sup>, 3.47 · 10<sup>-3</sup>]</td><td>2.39 · 10<sup>-4</sup>§</td></tr><tr><td>TR</td><td>1.29 · 10<sup>-3</sup></td><td>[8.30 · 10<sup>-4</sup>, 1.76 · 10<sup>-3</sup>]</td><td>1.40 · 10<sup>-6</sup>§</td></tr><tr><td rowspan="7">NOACT</td><td>SP</td><td>5.21 · 10<sup>-4</sup></td><td>[-3.61 · 10<sup>-4</sup>, 1.40 · 10<sup>-3</sup>]</td><td>2.70 · 10<sup>-1</sup></td></tr><tr><td>MC</td><td>-6.58 · 10<sup>-2</sup></td><td>[-1.80 · 10<sup>-1</sup>, 4.81 · 10<sup>-2</sup>]</td><td>2.76 · 10<sup>-1</sup></td></tr><tr><td>GE</td><td>4.71</td><td>[2.70, 6.72]</td><td>3.15 · 10<sup>-5</sup>§</td></tr><tr><td>ASP</td><td>-2.58 · 10<sup>-5</sup></td><td>[-3.87 · 10<sup>-5</sup>, -1.29 · 10<sup>-5</sup>]</td><td>2.63 · 10<sup>-4</sup>§</td></tr><tr><td>ACL</td><td>-3.78 · 10<sup>-5</sup></td><td>[-7.57 · 10<sup>-5</sup>, 1.09 · 10<sup>-7</sup>]</td><td>5.91 · 10<sup>-2</sup></td></tr><tr><td>AS</td><td>1.86 · 10<sup>-4</sup></td><td>[-5.58 · 10<sup>-4</sup>, 9.31 · 10<sup>-4</sup>]</td><td>6.41 · 10<sup>-1</sup></td></tr><tr><td>TR</td><td>-1.49 · 10<sup>-5</sup></td><td>[-5.33 · 10<sup>-4</sup>, 5.03 · 10<sup>-4</sup>]</td><td>9.54 · 10<sup>-1</sup></td></tr></tbody></table>
<table><caption>Table S2: Linear associations between global metrics and WMHB (Number of subjects = 50) by template and pipeline (slope with 95% CI and $R^2$). SP: sparsity, MC: mean connectivity, GE: global efficiency, ASP: average shortest path, ACL: average clustering, AS: assortativity, TR: transitivity.</caption><thead><tr><th rowspan="2">Clinical Pipeline</th><th rowspan="2">Metric</th><th colspan="3">MIITRA</th><th colspan="3">MNI</th></tr><tr><th>Slope</th><th>95% CI</th><th>R<sup>2</sup></th><th>Slope</th><th>95% CI</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="6">ATR-25-ACT</td><td>GE</td><td>-2.59</td><td>[-5.44,2.51 · 10<sup>-1</sup>]</td><td>0.097</td><td>-3.04</td><td>[-5.88,-1.90 · 10<sup>-1</sup>]</td><td>0.112</td></tr><tr><td>SP</td><td>9.49 · 10<sup>-3</sup></td><td>[5.09 · 10<sup>-3</sup>,1.39 · 10<sup>-2</sup>]</td><td>0.368</td><td>1.07 · 10<sup>-2</sup></td><td>[6.27 · 10<sup>-3</sup>,1.51 · 10<sup>-2</sup>]</td><td>0.467</td></tr><tr><td>MC</td><td>1.94</td><td>[1.22,2.65]</td><td>0.346</td><td>1.90</td><td>[1.19,2.61]</td><td>0.387</td></tr><tr><td>ASP</td><td>1.59 · 10<sup>-5</sup></td><td>[1.49 · 10<sup>-6</sup>,3.03 · 10<sup>-5</sup>]</td><td>0.160</td><td>1.79 · 10<sup>-5</sup></td><td>[3.51 · 10<sup>-6</sup>,3.24 · 10<sup>-5</sup>]</td><td>0.154</td></tr><tr><td>ACL</td><td>1.18 · 10<sup>-5</sup></td><td>[-2.46 · 10<sup>-6</sup>,2.61 · 10<sup>-5</sup>]</td><td>0.023</td><td>2.29 · 10<sup>-6</sup></td><td>[-1.20 · 10<sup>-5</sup>,1.66 · 10<sup>-5</sup>]</td><td>0.004</td></tr><tr><td>AS</td><td>2.80 · 10<sup>-3</sup></td><td>[8.91 · 10<sup>-4</sup>,4.71 · 10<sup>-3</sup>]</td><td>0.120</td><td>3.09 · 10<sup>-3</sup></td><td>[1.18 · 10<sup>-3</sup>,5.00 · 10<sup>-3</sup>]</td><td>0.190</td></tr><tr><td rowspan="6">ATR-30-ACT</td><td>TR</td><td>-2.96 · 10<sup>-3</sup></td><td>[-5.14 · 10<sup>-3</sup>,-7.70 · 10<sup>-4</sup>]</td><td>0.254</td><td>-3.04 · 10<sup>-3</sup></td><td>[-5.23 · 10<sup>-3</sup>,-8.58 · 10<sup>-4</sup>]</td><td>0.281</td></tr><tr><td>GE</td><td>-1.84</td><td>[-4.69,1.00]</td><td>0.065</td><td>-2.97</td><td>[-5.82,-1.25 · 10<sup>-1</sup>]</td><td>0.118</td></tr><tr><td>SP</td><td>7.30 · 10<sup>-3</sup></td><td>[2.90 · 10<sup>-3</sup>,1.17 · 10<sup>-2</sup>]</td><td>0.316</td><td>7.97 · 10<sup>-3</sup></td><td>[3.57 · 10<sup>-3</sup>,1.24 · 10<sup>-2</sup>]</td><td>0.364</td></tr><tr><td>MC</td><td>1.50</td><td>[7.87 · 10<sup>-1</sup>,2.21]</td><td>0.298</td><td>1.28</td><td>[5.64 · 10<sup>-1</sup>,1.99]</td><td>0.243</td></tr><tr><td>ASP</td><td>1.17 · 10<sup>-5</sup></td><td>[-2.68 · 10<sup>-6</sup>,2.62 · 10<sup>-5</sup>]</td><td>0.127</td><td>1.79 · 10<sup>-5</sup></td><td>[3.46 · 10<sup>-6</sup>,3.23 · 10<sup>-5</sup>]</td><td>0.168</td></tr><tr><td>ACL</td><td>6.82 · 10<sup>-6</sup></td><td>[-7.47 · 10<sup>-6</sup>,2.11 · 10<sup>-5</sup>]</td><td>0.002</td><td>-3.31 · 10<sup>-8</sup></td><td>[-1.43 · 10<sup>-5</sup>,1.43 · 10<sup>-5</sup>]</td><td>0.013</td></tr><tr><td rowspan="6">ATR-35-ACT</td><td>AS</td><td>1.29 · 10<sup>-3</sup></td><td>[-6.17 · 10<sup>-4</sup>,3.20 · 10<sup>-3</sup>]</td><td>0.034</td><td>1.64 · 10<sup>-3</sup></td><td>[-2.72 · 10<sup>-4</sup>,3.55 · 10<sup>-3</sup>]</td><td>0.064</td></tr><tr><td>TR</td><td>-2.21 · 10<sup>-3</sup></td><td>[-4.40 · 10<sup>-3</sup>,-2.66 · 10<sup>-5</sup>]</td><td>0.211</td><td>-2.40 · 10<sup>-3</sup></td><td>[-4.59 · 10<sup>-3</sup>,-2.13 · 10<sup>-4</sup>]</td><td>0.238</td></tr><tr><td>GE</td><td>-1.30</td><td>[-4.15,1.54]</td><td>0.046</td><td>-2.49</td><td>[-5.34,3.54 · 10<sup>-1</sup>]</td><td>0.098</td></tr><tr><td>SP</td><td>5.93 · 10<sup>-3</sup></td><td>[1.53 · 10<sup>-3</sup>,1.03 · 10<sup>-2</sup>]</td><td>0.285</td><td>6.68 · 10<sup>-3</sup></td><td>[2.28 · 10<sup>-3</sup>,1.11 · 10<sup>-2</sup>]</td><td>0.325</td></tr><tr><td>MC</td><td>1.22</td><td>[5.03 · 10<sup>-1</sup>,1.93]</td><td>0.262</td><td>1.06</td><td>[3.47 · 10<sup>-1</sup>,1.77]</td><td>0.212</td></tr><tr><td>ASP</td><td>8.74 · 10<sup>-6</sup></td><td>[-5.68 · 10<sup>-6</sup>,2.32 · 10<sup>-5</sup>]</td><td>0.105</td><td>1.50 · 10<sup>-5</sup></td><td>[6.04 · 10<sup>-7</sup>,2.94 · 10<sup>-5</sup>]</td><td>0.140</td></tr><tr><td rowspan="6">ATR-40-ACT</td><td>ACL</td><td>4.24 · 10<sup>-6</sup></td><td>[-1.01 · 10<sup>-5</sup>,1.85 · 10<sup>-5</sup>]</td><td>0.000</td><td>-2.89 · 10<sup>-6</sup></td><td>[-1.72 · 10<sup>-5</sup>,1.14 · 10<sup>-5</sup>]</td><td>0.029</td></tr><tr><td>AS</td><td>6.68 · 10<sup>-4</sup></td><td>[-1.24 · 10<sup>-3</sup>,2.58 · 10<sup>-3</sup>]</td><td>0.012</td><td>8.52 · 10<sup>-4</sup></td><td>[-1.06 · 10<sup>-3</sup>,2.76 · 10<sup>-3</sup>]</td><td>0.021</td></tr><tr><td>TR</td><td>-1.77 · 10<sup>-3</sup></td><td>[-3.96 · 10<sup>-3</sup>,4.14 · 10<sup>-4</sup>]</td><td>0.189</td><td>-1.98 · 10<sup>-3</sup></td><td>[-4.17 · 10<sup>-3</sup>,2.01 · 10<sup>-4</sup>]</td><td>0.208</td></tr><tr><td>GE</td><td>-9.66 · 10<sup>-1</sup></td><td>[-3.81,1.88]</td><td>0.032</td><td>-2.18</td><td>[-5.03,6.62 · 10<sup>-1</sup>]</td><td>0.079</td></tr><tr><td>SP</td><td>5.12 · 10<sup>-3</sup></td><td>[7.24 · 10<sup>-4</sup>,9.52 · 10<sup>-3</sup>]</td><td>0.262</td><td>5.77 · 10<sup>-3</sup></td><td>[1.38 · 10<sup>-3</sup>,1.02 · 10<sup>-2</sup>]</td><td>0.295</td></tr><tr><td>MC</td><td>1.06</td><td>[3.50 · 10<sup>-1</sup>,1.78]</td><td>0.244</td><td>8.86 · 10<sup>-1</sup></td><td>[1.74 · 10<sup>-1</sup>,1.60]</td><td>0.177</td></tr><tr><td rowspan="6">WMHB</td><td>ASP</td><td>7.06 · 10<sup>-6</sup></td><td>[-7.36 · 10<sup>-6</sup>,2.15 · 10<sup>-5</sup>]</td><td>0.084</td><td>1.34 · 10<sup>-5</sup></td><td>[-1.04 · 10<sup>-6</sup>,2.78 · 10<sup>-5</sup>]</td><td>0.115</td></tr><tr><td>ACL</td><td>1.77 · 10<sup>-6</sup></td><td>[-1.25 · 10<sup>-5</sup>,1.61 · 10<sup>-5</sup>]</td><td>0.007</td><td>-3.31 · 10<sup>-6</sup></td><td>[-1.76 · 10<sup>-5</sup>,1.10 · 10<sup>-5</sup>]</td><td>0.030</td></tr><tr><td>AS</td><td>1.73 · 10<sup>-4</sup></td><td>[-1.74 · 10<sup>-3</sup>,2.08 · 10<sup>-3</sup>]</td><td>0.001</td><td>5.04 · 10<sup>-4</sup></td><td>[-1.41 · 10<sup>-3</sup>,2.41 · 10<sup>-3</sup>]</td><td>0.008</td></tr><tr><td>TR</td><td>-1.67 · 10<sup>-3</sup></td><td>[-3.85 · 10<sup>-3</sup>,5.20 · 10<sup>-4</sup>]</td><td>0.182</td><td>-1.78 · 10<sup>-3</sup></td><td>[-3.97 · 10<sup>-3</sup>,4.04 · 10<sup>-4</sup>]</td><td>0.196</td></tr><tr><td>GE</td><td>-6.15 · 10<sup>-1</sup></td><td>[-3.46,2.23]</td><td>0.021</td><td>-1.68</td><td>[-4.52,1.17]</td><td>0.054</td></tr><tr><td>SP</td><td>4.89 · 10<sup>-3</sup></td><td>[4.89 · 10<sup>-4</sup>,9.29 · 10<sup>-3</sup>]</td><td>0.264</td><td>5.41 · 10<sup>-3</sup></td><td>[1.01 · 10<sup>-3</sup>,9.80 · 10<sup>-3</sup>]</td><td>0.289</td></tr><tr><td rowspan="6">ATR-45-ACT</td><td>MC</td><td>1.03</td><td>[3.20 · 10<sup>-1</sup>,1.74]</td><td>0.254</td><td>8.55 · 10<sup>-1</sup></td><td>[1.42 · 10<sup>-1</sup>,1.57]</td><td>0.187</td></tr><tr><td>ASP</td><td>4.99 · 10<sup>-6</sup></td><td>[-9.44 · 10<sup>-6</sup>,1.94 · 10<sup>-5</sup>]</td><td>0.064</td><td>1.07 · 10<sup>-5</sup></td><td>[-3.69 · 10<sup>-6</sup>,2.51 · 10<sup>-5</sup>]</td><td>0.086</td></tr><tr><td>ACL</td><td>7.33 · 10<sup>-7</sup></td><td>[-1.36 · 10<sup>-5</sup>,1.50 · 10<sup>-5</sup>]</td><td>0.012</td><td>-2.34 · 10<sup>-6</sup></td><td>[-1.66 · 10<sup>-5</sup>,1.20 · 10<sup>-5</sup>]</td><td>0.024</td></tr><tr><td>AS</td><td>-3.35 · 10<sup>-4</sup></td><td>[-2.24 · 10<sup>-3</sup>,1.57 · 10<sup>-3</sup>]</td><td>0.005</td><td>3.14 · 10<sup>-4</sup></td><td>[-1.60 · 10<sup>-3</sup>,2.22 · 10<sup>-3</sup>]</td><td>0.004</td></tr><tr><td>TR</td><td>-1.69 · 10<sup>-3</sup></td><td>[-3.87 · 10<sup>-3</sup>,4.99 · 10<sup>-4</sup>]</td><td>0.187</td><td>-1.77 · 10<sup>-3</sup></td><td>[-3.96 · 10<sup>-3</sup>,4.12 · 10<sup>-4</sup>]</td><td>0.195</td></tr><tr><td>GE</td><td>-1.83 · 10<sup>-1</sup></td><td>[-3.03,2.66]</td><td>0.009</td><td>-9.80 · 10<sup>-1</sup></td><td>[-3.83,1.87]</td><td>0.029</td></tr><tr><td>SP</td><td>4.66 · 10<sup>-3</sup></td><td>[2.66 · 10<sup>-4</sup>,9.06 · 10<sup>-3</sup>]</td><td>0.258</td><td>5.40 · 10<sup>-3</sup></td><td>[1.00 · 10<sup>-3</sup>,9.80 · 10<sup>-3</sup>]</td><td>0.294</td></tr><tr><td rowspan="6">ATR-50-ACT</td><td>MC</td><td>9.93 · 10<sup>-1</sup></td><td>[2.80 · 10<sup>-1</sup>,1.71]</td><td>0.255</td><td>9.09 · 10<sup>-1</sup></td><td>[1.97 · 10<sup>-1</sup>,1.62]</td><td>0.225</td></tr><tr><td>ASP</td><td>2.74 · 10<sup>-6</sup></td><td>[-1.17 · 10<sup>-5</sup>,1.72 · 10<sup>-5</sup>]</td><td>0.043</td><td>6.93 · 10<sup>-6</sup></td><td>[-7.49 · 10<sup>-6</sup>,2.14 · 10<sup>-5</sup>]</td><td>0.051</td></tr><tr><td>ACL</td><td>-2.30 · 10<sup>-8</sup></td><td>[-1.43 · 10<sup>-5</sup>,1.43 · 10<sup>-5</sup>]</td><td>0.016</td><td>-1.74 · 10<sup>-7</sup></td><td>[-1.45 · 10<sup>-5</sup>,1.41 · 10<sup>-5</sup>]</td><td>0.013</td></tr><tr><td>AS</td><td>-2.45 · 10<sup>-4</sup></td><td>[-2.15 · 10<sup>-3</sup>,1.66 · 10<sup>-3</sup>]</td><td>0.003</td><td>2.78 · 10<sup>-4</sup></td><td>[-1.63 · 10<sup>-3</sup>,2.19 · 10<sup>-3</sup>]</td><td>0.003</td></tr><tr><td>TR</td><td>-1.77 · 10<sup>-3</sup></td><td>[-3.96 · 10<sup>-3</sup>,4.16 · 10<sup>-4</sup>]</td><td>0.191</td><td>-1.98 · 10<sup>-3</sup></td><td>[-4.17 · 10<sup>-3</sup>,2.07 · 10<sup>-4</sup>]</td><td>0.216</td></tr><tr><td>GE</td><td>-3.60</td><td>[-6.45,-7.58 · 10<sup>-1</sup>]</td><td>0.154</td><td>-2.97</td><td>[-5.82,-1.28 · 10<sup>-1</sup>]</td><td>0.114</td></tr><tr><td>SP</td><td>2.39 · 10<sup>-2</sup></td><td>[1.95 · 10<sup>-2</sup>,2.83 · 10<sup>-2</sup>]</td><td>0.673</td><td>2.40 · 10<sup>-2</sup></td><td>[1.96 · 10<sup>-2</sup>,2.84 · 10<sup>-2</sup>]</td><td>0.672</td></tr><tr><td rowspan="6">FAST-ACT</td><td>MC</td><td>7.08</td><td>[6.37,7.79]</td><td>0.909</td><td>7.20</td><td>[6.49,7.91]</td><td>0.911</td></tr><tr><td>ASP</td><td>2.26 · 10<sup>-5</sup></td><td>[8.19 · 10<sup>-6</sup>,3.70 · 10<sup>-5</sup>]</td><td>0.214</td><td>1.77 · 10<sup>-5</sup></td><td>[3.26 · 10<sup>-6</sup>,3.21 · 10<sup>-5</sup>]</td><td>0.147</td></tr><tr><td>ACL</td><td>5.12 · 10<sup>-5</sup></td><td>[3.69 · 10<sup>-5</sup>,6.55 · 10<sup>-5</sup>]</td><td>0.515</td><td>3.86 · 10<sup>-5</sup></td><td>[2.44 · 10<sup>-5</sup>,5.29 · 10<sup>-5</sup>]</td><td>0.329</td></tr><tr><td>AS</td><td>8.34 · 10<sup>-3</sup></td><td>[6.44 · 10<sup>-3</sup>,1.03 · 10<sup>-2</sup>]</td><td>0.538</td><td>8.41 · 10<sup>-3</sup></td><td>[6.50 · 10<sup>-3</sup>,1.03 · 10<sup>-2</sup>]</td><td>0.569</td></tr><tr><td>TR</td><td>-9.49 · 10<sup>-3</sup></td><td>[-1.17 · 10<sup>-2</sup>,-7.30 · 10<sup>-3</sup>]</td><td>0.512</td><td>-9.46 · 10<sup>-3</sup></td><td>[-1.16 · 10<sup>-2</sup>,-7.27 · 10<sup>-3</sup>]</td><td>0.506</td></tr><tr><td rowspan="6">NOACT</td><td>GE</td><td>4.01</td><td>[1.16,6.86]</td><td>0.224</td><td>4.21</td><td>[1.36,7.06]</td><td>0.254</td></tr><tr><td>SP</td><td>2.27 · 10<sup>-3</sup></td><td>[-2.13 · 10<sup>-3</sup>,6.66 · 10<sup>-3</sup>]</td><td>0.275</td><td>2.34 · 10<sup>-3</sup></td><td>[-2.06 · 10<sup>-3</sup>,6.73 · 10<sup>-3</sup>]</td><td>0.275</td></tr><tr><td>MC</td><td>4.16 · 10<sup>-1</sup></td><td>[-2.96 · 10<sup>-1</sup>,1.13]</td><td>0.303</td><td>4.39 · 10<sup>-1</sup></td><td>[-2.74 · 10<sup>-1</sup>,1.15]</td><td>0.314</td></tr><tr><td>ASP</td><td>-1.84 · 10<sup>-5</sup></td><td>[-3.28 · 10<sup>-5</sup>,-3.95 · 10<sup>-6</sup>]</td><td>0.106</td><td>-2.00 · 10<sup>-5</sup></td><td>[-3.44 · 10<sup>-5</sup>,-5.60 · 10<sup>-6</sup>]</td><td>0.132</td></tr><tr><td>ACL</td><td>9.55 · 10<sup>-7</sup></td><td>[-1.33 · 10<sup>-5</sup>,1.52 · 10<sup>-5</sup>]</td><td>0.005</td><td>-1.12 · 10<sup>-6</sup></td><td>[-1.54 · 10<sup>-5</sup>,1.32 · 10<sup>-5</sup>]</td><td>0.013</td></tr><tr><td>AS</td><td>1.06 · 10<sup>-3</sup></td><td>[-8.48 · 10<sup>-4</sup>,2.97 · 10<sup>-3</sup>]</td><td>0.090</td><td>9.67 · 10<sup>-4</sup></td><td>[-9.43 · 10<sup>-4</sup>,2.88 · 10<sup>-3</sup>]</td><td>0.071</td></tr><tr><td rowspan="6">NOACT</td><td>TR</td><td>-1.85 · 10<sup>-3</sup></td><td>[-4.04 · 10<sup>-3</sup>,3.35 · 10<sup>-4</sup>]</td><td>0.274</td><td>-1.90 · 10<sup>-3</sup></td><td>[-4.08 · 10<sup>-3</sup>,2.88 · 10<sup>-4</sup>]</td><td>0.276</td></tr></tbody></table>
<table><caption>Table S4: Linear associations between global metrics and MoCA score (Number of subjects = 49) by template and pipeline (slope with 95% CI and $R^2$). SP: sparsity, MC: mean connectivity, GE: global efficiency, ASP: average shortest path, ACL: average clustering, AS: assortativity, TR: transitivity.</caption><thead><tr><th rowspan="2">Clinical Pipeline</th><th rowspan="2">Metric</th><th colspan="3">MIITRA</th><th colspan="3">MNI</th></tr><tr><th>Slope</th><th>95% CI</th><th>R<sup>2</sup></th><th>Slope</th><th>95% CI</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td rowspan="6">ATR-25-ACT</td><td>GE</td><td>1.24</td><td>[-2.44,4.91]</td><td>0.006</td><td>2.24</td><td>[-1.43,5.92]</td><td>0.022</td></tr><tr><td>SP</td><td>-3.66 · 10<sup>-3</sup></td><td>[-1.03 · 10<sup>-2</sup>,2.96 · 10<sup>-3</sup>]</td><td>0.006</td><td>-3.69 · 10<sup>-3</sup></td><td>[-1.03 · 10<sup>-2</sup>,2.94 · 10<sup>-3</sup>]</td><td>0.007</td></tr><tr><td>MC</td><td>-1.87 · 10<sup>-1</sup></td><td>[-1.53,1.16]</td><td>0.001</td><td>-4.69 · 10<sup>-2</sup></td><td>[-1.39,1.30]</td><td>0.004</td></tr><tr><td>ASP</td><td>-3.08 · 10<sup>-6</sup></td><td>[-2.21 · 10<sup>-5</sup>,1.59 · 10<sup>-5</sup>]</td><td>0.001</td><td>-6.75 · 10<sup>-6</sup></td><td>[-2.57 · 10<sup>-5</sup>,1.22 · 10<sup>-5</sup>]</td><td>0.007</td></tr><tr><td>ACL</td><td>5.94 · 10<sup>-6</sup></td><td>[-1.30 · 10<sup>-5</sup>,2.49 · 10<sup>-5</sup>]</td><td>0.032</td><td>3.83 · 10<sup>-6</sup></td><td>[-1.51 · 10<sup>-5</sup>,2.28 · 10<sup>-5</sup>]</td><td>0.020</td></tr><tr><td>AS</td><td>4.09 · 10<sup>-4</sup></td><td>[-2.25 · 10<sup>-3</sup>,3.07 · 10<sup>-3</sup>]</td><td>0.009</td><td>-6.98 · 10<sup>-5</sup></td><td>[-2.73 · 10<sup>-3</sup>,2.59 · 10<sup>-3</sup>]</td><td>0.003</td></tr><tr><td rowspan="6">ATR-30-ACT</td><td>TR</td><td>1.43 · 10<sup>-3</sup></td><td>[-1.64 · 10<sup>-3</sup>,4.50 · 10<sup>-3</sup>]</td><td>0.005</td><td>1.23 · 10<sup>-3</sup></td><td>[-1.84 · 10<sup>-3</sup>,4.30 · 10<sup>-3</sup>]</td><td>0.003</td></tr><tr><td>GE</td><td>7.13 · 10<sup>-1</sup></td><td>[-2.96,4.39]</td><td>0.002</td><td>1.85</td><td>[-1.83,5.52]</td><td>0.015</td></tr><tr><td>SP</td><td>-2.40 · 10<sup>-3</sup></td><td>[-9.03 · 10<sup>-3</sup>,4.22 · 10<sup>-3</sup>]</td><td>0.001</td><td>-2.55 · 10<sup>-3</sup></td><td>[-9.17 · 10<sup>-3</sup>,4.08 · 10<sup>-3</sup>]</td><td>0.002</td></tr><tr><td>MC</td><td>-9.49 · 10<sup>-2</sup></td><td>[-1.44,1.25]</td><td>0.003</td><td>7.55 · 10<sup>-2</sup></td><td>[-1.27,1.42]</td><td>0.010</td></tr><tr><td>ASP</td><td>-4.16 · 10<sup>-7</sup></td><td>[-1.94 · 10<sup>-5</sup>,1.86 · 10<sup>-5</sup>]</td><td>0.000</td><td>-4.91 · 10<sup>-6</sup></td><td>[-2.39 · 10<sup>-5</sup>,1.41 · 10<sup>-5</sup>]</td><td>0.004</td></tr><tr><td>ACL</td><td>6.55 · 10<sup>-6</sup></td><td>[-1.24 · 10<sup>-5</sup>,2.55 · 10<sup>-5</sup>]</td><td>0.040</td><td>5.29 · 10<sup>-6</sup></td><td>[-1.36 · 10<sup>-5</sup>,2.42 · 10<sup>-5</sup>]</td><td>0.026</td></tr><tr><td rowspan="6">ATR-35-ACT</td><td>AS</td><td>5.50 · 10<sup>-4</sup></td><td>[-2.11 · 10<sup>-3</sup>,3.21 · 10<sup>-3</sup>]</td><td>0.016</td><td>6.44 · 10<sup>-4</sup></td><td>[-2.02 · 10<sup>-3</sup>,3.31 · 10<sup>-3</sup>]</td><td>0.022</td></tr><tr><td>TR</td><td>7.79 · 10<sup>-4</sup></td><td>[-2.29 · 10<sup>-3</sup>,3.85 · 10<sup>-3</sup>]</td><td>0.000</td><td>7.60 · 10<sup>-4</sup></td><td>[-2.31 · 10<sup>-3</sup>,3.83 · 10<sup>-3</sup>]</td><td>0.000</td></tr><tr><td>GE</td><td>2.54 · 10<sup>-1</sup></td><td>[-3.42,3.93]</td><td>0.000</td><td>1.33</td><td>[-2.34,5.01]</td><td>0.008</td></tr><tr><td>SP</td><td>-1.74 · 10<sup>-3</sup></td><td>[-8.36 · 10<sup>-3</sup>,4.89 · 10<sup>-3</sup>]</td><td>0.000</td><td>-2.08 · 10<sup>-3</sup></td><td>[-8.71 · 10<sup>-3</sup>,4.54 · 10<sup>-3</sup>]</td><td>0.001</td></tr><tr><td>MC</td><td>-7.02 · 10<sup>-2</sup></td><td>[-1.42,1.28]</td><td>0.004</td><td>7.32 · 10<sup>-2</sup></td><td>[-1.27,1.42]</td><td>0.012</td></tr><tr><td>ASP</td><td>1.45 · 10<sup>-6</sup></td><td>[-1.75 · 10<sup>-5</sup>,2.04 · 10<sup>-5</sup>]</td><td>0.001</td><td>-2.36 · 10<sup>-6</sup></td><td>[-2.13 · 10<sup>-5</sup>,1.66 · 10<sup>-5</sup>]</td><td>0.001</td></tr><tr><td rowspan="6">ATR-40-ACT</td><td>ACL</td><td>6.48 · 10<sup>-6</sup></td><td>[-1.24 · 10<sup>-5</sup>,2.54 · 10<sup>-5</sup>]</td><td>0.043</td><td>6.35 · 10<sup>-6</sup></td><td>[-1.26 · 10<sup>-5</sup>,2.53 · 10<sup>-5</sup>]</td><td>0.030</td></tr><tr><td>AS</td><td>5.88 · 10<sup>-4</sup></td><td>[-2.08 · 10<sup>-3</sup>,3.25 · 10<sup>-3</sup>]</td><td>0.022</td><td>3.96 · 10<sup>-4</sup></td><td>[-2.27 · 10<sup>-3</sup>,3.06 · 10<sup>-3</sup>]</td><td>0.017</td></tr><tr><td>TR</td><td>4.05 · 10<sup>-4</sup></td><td>[-2.66 · 10<sup>-3</sup>,3.47 · 10<sup>-3</sup>]</td><td>0.001</td><td>5.41 · 10<sup>-4</sup></td><td>[-2.53 · 10<sup>-3</sup>,3.61 · 10<sup>-3</sup>]</td><td>0.000</td></tr><tr><td>GE</td><td>-6.39 · 10<sup>-2</sup></td><td>[-3.74,3.61]</td><td>0.000</td><td>1.57</td><td>[-2.11,5.24]</td><td>0.012</td></tr><tr><td>SP</td><td>-1.32 · 10<sup>-3</sup></td><td>[-7.94 · 10<sup>-3</sup>,5.30 · 10<sup>-3</sup>]</td><td>0.000</td><td>-1.65 · 10<sup>-3</sup></td><td>[-8.27 · 10<sup>-3</sup>,4.97 · 10<sup>-3</sup>]</td><td>0.000</td></tr><tr><td>MC</td><td>-6.24 · 10<sup>-2</sup></td><td>[-1.41,1.29]</td><td>0.006</td><td>1.63 · 10<sup>-1</sup></td><td>[-1.18,1.51]</td><td>0.021</td></tr><tr><td rowspan="6">MoCA</td><td>ASP</td><td>2.90 · 10<sup>-6</sup></td><td>[-1.61 · 10<sup>-5</sup>,2.19 · 10<sup>-5</sup>]</td><td>0.004</td><td>-4.11 · 10<sup>-6</sup></td><td>[-2.31 · 10<sup>-5</sup>,1.49 · 10<sup>-5</sup>]</td><td>0.003</td></tr><tr><td>ACL</td><td>7.08 · 10<sup>-6</sup></td><td>[-1.18 · 10<sup>-5</sup>,2.60 · 10<sup>-5</sup>]</td><td>0.049</td><td>7.80 · 10<sup>-6</sup></td><td>[-1.11 · 10<sup>-5</sup>,2.67 · 10<sup>-5</sup>]</td><td>0.038</td></tr><tr><td>AS</td><td>5.19 · 10<sup>-4</sup></td><td>[-2.14 · 10<sup>-3</sup>,3.18 · 10<sup>-3</sup>]</td><td>0.023</td><td>6.83 · 10<sup>-4</sup></td><td>[-1.98 · 10<sup>-3</sup>,3.35 · 10<sup>-3</sup>]</td><td>0.033</td></tr><tr><td>TR</td><td>1.65 · 10<sup>-4</sup></td><td>[-2.90 · 10<sup>-3</sup>,3.23 · 10<sup>-3</sup>]</td><td>0.004</td><td>3.17 · 10<sup>-4</sup></td><td>[-2.75 · 10<sup>-3</sup>,3.39 · 10<sup>-3</sup>]</td><td>0.002</td></tr><tr><td>GE</td><td>-9.36 · 10<sup>-2</sup></td><td>[-3.77,3.58]</td><td>0.001</td><td>1.51</td><td>[-2.17,5.18]</td><td>0.011</td></tr><tr><td>SP</td><td>-1.15 · 10<sup>-3</sup></td><td>[-7.77 · 10<sup>-3</sup>,5.48 · 10<sup>-3</sup>]</td><td>0.001</td><td>-1.72 · 10<sup>-3</sup></td><td>[-8.34 · 10<sup>-3</sup>,4.91 · 10<sup>-3</sup>]</td><td>0.000</td></tr><tr><td rowspan="6">FAST-ACT</td><td>MC</td><td>-8.23 · 10<sup>-2</sup></td><td>[-1.43,1.27]</td><td>0.005</td><td>1.30 · 10<sup>-1</sup></td><td>[-1.22,1.48]</td><td>0.020</td></tr><tr><td>ASP</td><td>2.94 · 10<sup>-6</sup></td><td>[-1.60 · 10<sup>-5</sup>,2.19 · 10<sup>-5</sup>]</td><td>0.005</td><td>-3.99 · 10<sup>-6</sup></td><td>[-2.30 · 10<sup>-5</sup>,1.50 · 10<sup>-5</sup>]</td><td>0.002</td></tr><tr><td>ACL</td><td>7.06 · 10<sup>-6</sup></td><td>[-1.19 · 10<sup>-5</sup>,2.60 · 10<sup>-5</sup>]</td><td>0.047</td><td>5.65 · 10<sup>-6</sup></td><td>[-1.33 · 10<sup>-5</sup>,2.46 · 10<sup>-5</sup>]</td><td>0.025</td></tr><tr><td>AS</td><td>6.55 · 10<sup>-4</sup></td><td>[-2.01 · 10<sup>-3</sup>,3.32 · 10<sup>-3</sup>]</td><td>0.037</td><td>1.60 · 10<sup>-4</sup></td><td>[-2.50 · 10<sup>-3</sup>,2.82 · 10<sup>-3</sup>]</td><td>0.014</td></tr><tr><td>TR</td><td>1.74 · 10<sup>-4</sup></td><td>[-2.89 · 10<sup>-3</sup>,3.24 · 10<sup>-3</sup>]</td><td>0.004</td><td>3.16 · 10<sup>-4</sup></td><td>[-2.75 · 10<sup>-3</sup>,3.38 · 10<sup>-3</sup>]</td><td>0.002</td></tr><tr><td rowspan="6">ATR-50-ACT</td><td>GE</td><td>-2.23 · 10<sup>-1</sup></td><td>[-3.90,3.45]</td><td>0.001</td><td>1.33</td><td>[-2.35,5.00]</td><td>0.009</td></tr><tr><td>SP</td><td>-1.02 · 10<sup>-3</sup></td><td>[-7.65 · 10<sup>-3</sup>,5.60 · 10<sup>-3</sup>]</td><td>0.001</td><td>-1.51 · 10<sup>-3</sup></td><td>[-8.14 · 10<sup>-3</sup>,5.11 · 10<sup>-3</sup>]</td><td>0.000</td></tr><tr><td>MC</td><td>-9.74 · 10<sup>-2</sup></td><td>[-1.45,1.25]</td><td>0.005</td><td>8.89 · 10<sup>-2</sup></td><td>[-1.26,1.44]</td><td>0.018</td></tr><tr><td>ASP</td><td>3.19 · 10<sup>-6</sup></td><td>[-1.58 · 10<sup>-5</sup>,2.22 · 10<sup>-5</sup>]</td><td>0.006</td><td>-3.79 · 10<sup>-6</sup></td><td>[-2.28 · 10<sup>-5</sup>,1.52 · 10<sup>-5</sup>]</td><td>0.002</td></tr><tr><td>ACL</td><td>7.10 · 10<sup>-6</sup></td><td>[-1.18 · 10<sup>-5</sup>,2.60 · 10<sup>-5</sup>]</td><td>0.046</td><td>1.02 · 10<sup>-6</sup></td><td>[-1.79 · 10<sup>-5</sup>,1.99 · 10<sup>-5</sup>]</td><td>0.008</td></tr><tr><td>AS</td><td>5.38 · 10<sup>-4</sup></td><td>[-2.12 · 10<sup>-3</sup>,3.20 · 10<sup>-3</sup>]</td><td>0.035</td><td>4.16 · 10<sup>-4</sup></td><td>[-2.25 · 10<sup>-3</sup>,3.08 · 10<sup>-3</sup>]</td><td>0.028</td></tr><tr><td rowspan="6">NOACT</td><td>TR</td><td>6.01 · 10<sup>-5</sup></td><td>[-3.01 · 10<sup>-3</sup>,3.13 · 10<sup>-3</sup>]</td><td>0.005</td><td>2.35 · 10<sup>-4</sup></td><td>[-2.83 · 10<sup>-3</sup>,3.30 · 10<sup>-3</sup>]</td><td>0.003</td></tr><tr><td>GE</td><td>2.71</td><td>[-9.70 · 10<sup>-1</sup>,6.38]</td><td>0.033</td><td>2.76</td><td>[-9.12 · 10<sup>-1</sup>,6.44]</td><td>0.036</td></tr><tr><td>SP</td><td>-7.73 · 10<sup>-3</sup></td><td>[-1.44 · 10<sup>-2</sup>,-1.11 · 10<sup>-3</sup>]</td><td>0.021</td><td>-7.68 · 10<sup>-3</sup></td><td>[-1.43 · 10<sup>-2</sup>,-1.06 · 10<sup>-3</sup>]</td><td>0.021</td></tr><tr><td>MC</td><td>-1.16</td><td>[-2.51,1.87 · 10<sup>-1</sup>]</td><td>0.007</td><td>-1.19</td><td>[-2.54,1.58 · 10<sup>-1</sup>]</td><td>0.008</td></tr><tr><td>ASP</td><td>-1.14 · 10<sup>-5</sup></td><td>[-3.03 · 10<sup>-5</sup>,7.62 · 10<sup>-6</sup>]</td><td>0.019</td><td>-1.16 · 10<sup>-5</sup></td><td>[-3.06 · 10<sup>-5</sup>,7.34 · 10<sup>-6</sup>]</td><td>0.021</td></tr><tr><td>ACL</td><td>3.66 · 10<sup>-7</sup></td><td>[-1.86 · 10<sup>-5</sup>,1.93 · 10<sup>-5</sup>]</td><td>0.003</td><td>-1.90 · 10<sup>-6</sup></td><td>[-2.08 · 10<sup>-5</sup>,1.70 · 10<sup>-5</sup>]</td><td>0.001</td></tr><tr><td rowspan="6">NOACT</td><td>AS</td><td>-1.62 · 10<sup>-4</sup></td><td>[-2.83 · 10<sup>-3</sup>,2.50 · 10<sup>-3</sup>]</td><td>0.001</td><td>-1.47 · 10<sup>-4</sup></td><td>[-2.81 · 10<sup>-3</sup>,2.52 · 10<sup>-3</sup>]</td><td>0.001</td></tr><tr><td>TR</td><td>3.57 · 10<sup>-3</sup></td><td>[5.04 · 10<sup>-4</sup>,6.64 · 10<sup>-3</sup>]</td><td>0.022</td><td>3.54 · 10<sup>-3</sup></td><td>[4.68 · 10<sup>-4</sup>,6.61 · 10<sup>-3</sup>]</td><td>0.021</td></tr><tr><td>GE</td><td>2.29</td><td>[-1.39,5.96]</td><td>0.049</td><td>1.66</td><td>[-2.02,5.33]</td><td>0.025</td></tr><tr><td>SP</td><td>-4.21 · 10<sup>-3</sup></td><td>[-1.08 · 10<sup>-2</sup>,2.41 · 10<sup>-3</sup>]</td><td>0.046</td><td>-4.24 · 10<sup>-3</sup></td><td>[-1.09 · 10<sup>-2</sup>,2.38 · 10<sup>-3</sup>]</td><td>0.046</td></tr><tr><td>MC</td><td>-3.59 · 10<sup>-1</sup></td><td>[-1.71,9.89 · 10<sup>-1</sup>]</td><td>0.001</td><td>-3.95 · 10<sup>-1</sup></td><td>[-1.74,9.52 · 10<sup>-1</sup>]</td><td>0.004</td></tr><tr><td>ASP</td><td>-1.25 · 10<sup>-5</sup></td><td>[-3.15 · 10<sup>-5</sup>,6.49 · 10<sup>-6</sup>]</td><td>0.048</td><td>-7.50 · 10<sup>-6</sup></td><td>[-2.65 · 10<sup>-5</sup>,1.15 · 10<sup>-5</sup>]</td><td>0.016</td></tr><tr><td rowspan="6">NOACT</td><td>ACL</td><td>-2.93 · 10<sup>-5</sup></td><td>[-4.82 · 10<sup>-5</sup>,-1.04 · 10<sup>-5</sup>]</td><td>0.115</td><td>-1.89 · 10<sup>-5</sup></td><td>[-3.78 · 10<sup>-5</sup>,4.27 · 10<sup>-9</sup>]</td><td>0.043</td></tr><tr><td>AS</td><td>-1.08 · 10<sup>-3</sup></td><td>[-3.74 · 10<sup>-3</sup>,1.59 · 10<sup>-3</sup>]</td><td>0.011</td><td>-1.17 · 10<sup>-3</sup></td><td>[-3.83 · 10<sup>-3</sup>,1.49 · 10<sup>-3</sup>]</td><td>0.014</td></tr><tr><td>TR</td><td>2.18 · 10<sup>-3</sup></td><td>[-8.85 · 10<sup>-4</sup>,5.25 · 10<sup>-3</sup>]</td><td>0.038</td><td>2.21 · 10<sup>-3</sup></td><td>[-8.64 · 10<sup>-4</sup>,5.27 · 10<sup>-3</sup>]</td><td>0.038</td></tr></tbody></table>