# Multi-compartment tractometry approach for white matter neuroinflammation investigation in late-life depression

Nathan Decaux, Gabriel Robert, Julie Coloigner

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Nathan Decaux, Gabriel Robert, Julie Coloigner. Multi-compartment tractometry approach for white matter neuroinflammation investigation in late-life depression. IST 2025 - International Society for Tractography Conference, Oct 2025, Bordeaux, France. hal-05458484

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https://hal.science/hal-05458484v1

Submitted on 14 Jan 2026

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## INTRODUCTION

Late-life depression (LLD) affects 7% of the population aged more than 60 years old. This prevalence is concerning, as LLD is not only an independent risk factor for mortality, but also a modifiable risk factor for dementia. The pathophysiology of LLD is multifactorial, involving inflammatory, degenerative, and vascular mechanisms. This complexity underscores the need for a deeper understanding of the underlying biological processes. Recent advances in diffusion imaging and tractography have led to the development of a novel framework—tractometry—for more precise assessment of white matter (WM) microstructure [1, 2]. In brief, these along-tract approaches generate a profile for each fiber bundle, mapping microstructural metrics onto a central streamline. Statistical analysis of these bundle profiles can provide a more specific and localized investigation than looking at a region of interest or tract-averaged measures. We hypothesize that a tract-based approach could yield novel biomarkers for LLD. Building on the work of Hédouin et al. [3], we performed an advanced microstructural compartment model (MCM) along fiber bundles, to derive more interpretable and sensitive metrics, of white matter integrity and inflammation that could not be obtained with a classical DTI model. We notably applied our framework on two metrics: free-water (FW) and fractional anisotropy (FA) between LLD and control groups.

## METHODS

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Illustration of the tractometry principle combining multi-compartment modeling (MCM) and tractography for microstructural analysis of white matter fiber bundles.</figcaption></figure>

We used the AMYNET database, comprising 31 patients with late-life depression (40 females, age 75.4 ± 6.2 years) and 21 healthy controls (16 females, age 74.8 ± 5.2 years), recruited from the French old-age psychiatry centers of Rennes and Tours, France. Diffusion MRI preprocessing followed the protocol established by Hédouin et al. [3], including denoising, Gibbs ringing correction, motion and eddy current correction, and bias field correction. T1-weighted images were used for anatomical reference and registration. Orientation distribution functions (ODFs) were estimated using the Multi-Shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD) algorithm implemented in MRtrix3 [7]. Tractography was performed using the iFOD2 algorithm with a target number of 1 million streamlines, seeded from the whole brain mask. Fiber bundle elements were extracted for each voxels following [9] approach and the MRTrix3 implementation. Main direction peaks were then derived from the fixels. A multi-compartment model (MCM) was also performed including one isotropic compartment representing free water, one iso-restricted compartment, and one to three anisotropic compartments, corresponding to distinct fiber populations. This number corresponds to detected peak directions of each ODF. From this model, we derived several metrics for each voxel including free water fraction (FW), iso-restricted water fraction (IRW), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) for each anisotropic compartment. White matter tracts were segmented using BundleSeg, an enhanced version of the RecoBundles algorithm [5] with a fixed search radius of 8mm. We performed the segmentation on each subject's tractogram using a white matter bundle atlas derived from the cohort of 105 annotated Human Connectome Project (HCP) data [6]. This template was constructed through an iterative registration process that aligned individual HCP subject data to a common template using a diffeomorphic approach, adapting the method proposed by [7] for diffusion tensor images. The resulting atlas contains 72 bundle models, each comprising streamlines from all atlas subjects registered to a standardized space. For complete methodological details regarding atlas creation, please refer to section 2.1 of [8]. We developed a tractometry approach that selects the anisotropic compartment most aligned with the local streamline direction at each point, rather than averaging all compartments as proposed in [3]. This preserves microstructural specificity in complex white matter regions. Microstructural metrics from the selected compartment were sampled at equidistant points along each bundle to create profiles for statistical comparison, as illustrated in Fig. 1. We performed a two-sample t-test for the FA and the FW between LLD patient and control groups with age, gender and site of MRI acquisition as covariates, correcting for multiple comparisons.

## RESULTS AND DISCUSSION

Among the 72 segmented bundles, six demonstrated significant group differences, as illustrated in Figure 2. The late-life depression (LLD) group exhibited elevated free water (FW) fractions in the left anterior thalamic radiation (ATR), left fronto-pontine tract (FPT), third segment of the corpus callosum (CC_3) and right thalamo-premotor tract (T-PREM) compared to healthy controls. The FW metric quantifies the relative fraction of freely diffusing water in the extracellular space, serving as a proxy for chronic low-grade neuroinflammation, which plays a pivotal role in the neuropathogenesis of various neurological and psychiatric disorders, including LLD. Consistent with previous research, we observed alterations in frontolimbic circuits implicated in LLD pathophysiology [3]. Additional findings such as decrease in FW of the right posterior parieto-occipital tract (POPT) and right inferior cerebellar peduncle (ICP) can likely be attributed to our multi-compartment modeling approach, which employs more realistic compartment estimations through fixel-based derivation. Furthermore, patients demonstrated significantly increased fractional anisotropy (FA) in the genu of the corpus callosum, while exhibiting decreased FA in the right inferior cerebellar peduncle—findings that diverge from previous studies. This discrepancy in results may be explained by our selection mechanism for identifying the anisotropic compartment most collinear with each tract.

<figure><img src="image_3.png" /><figcaption>**Figure 2:** Tractometry analysis showing group differences in free water fraction (FW) between controls (blue) and patients (orange) across six white matter tracts. Top: 3D tract reconstructions with significant regions highlighted in red. Bottom: Along-tract profiles with statistical comparisons (red dashed boxes indicate significant differences, FWE-corrected *p* < 0.05).</figcaption></figure>

## DISCUSSION AND CONCLUSION

The difference in the neuroinflammation component value between LLD and HC in the ATR and T-PREM confirms previous results reporting an association between these tracts and serum inflammation in major depressive disorder. Moreover, those prefrontal corticostriatal loops are known to be involved in goal-oriented behavior, mediating motor behavior such as planning, learning and motor execution.

Our future plans include exploring new approaches for projecting microstructural metrics along fiber bundles which address cases where central line reduction is not appropriate, such as at fiber bundle extremities, with the aim of more precisely investigating altered regions in patient populations. Additionally, we plan to investigate correlations between these structural findings and functional data, such as actigraphy, to better understand the underlying mechanisms of apathy and late-life depression.

**REFERENCES:** [1] Chamberland, M. et al. NeuroImage 200, 89–100 (2019). [2] Chandio, B. Q. et al. Sci Rep 10, 17149 (2020). [3] Hédouin, R., Roy, J.-C., Desmidt, T., Robert, G. & Coloigner, J. Sci Rep 14, 18193 (2024). [4] Smith, R. E., Tournier, J.-D., Calamante, F. & Connelly, A. NeuroImage 67, 298–312 (2013). [5] St-Onge, E., Schilling, K. G. & Rheault, F. (2023). doi:10.48550/arXiv.2308.10958 [6] Jakob, W., Peter, N. & Klaus, M.-H. (2018). doi:10.5281/zenodo.1477956 [7] Guimond, A., Meunier, J. & Thirion, J.-P. Computer Vision and Image Understanding 77, 192–210 (2000). [8] Durantel, T., Girard, G., Caruyer, E., Commowick, O. & Coloigner, J. PLOS ONE 20, e0304449 (2025). [9] Tournier, J. D., Calamante, F., & Connelly, A. Proceedings of the international society for magnetic resonance in medicine, Vol. 1670 (2010).