# Texture features of T2w magnetic resonance imaging mirror tissue changes in Parkinson's disease models.

Chirine El Katrib, Camille Naidji, David Devos, Kelly Timmerman, Nicolas Durieux, Nathalie Dutheil, Erwan Bezard, Charlotte Laloux, Nacim Betrouni

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Chirine El Katrib, Camille Naidji, David Devos, Kelly Timmerman, Nicolas Durieux, et al. Texture features of T2w magnetic resonance imaging mirror tissue changes in Parkinson's disease models. Brain research, 2025, Brain research, 1862, pp.149717. 10.1016/j.brainres.2025.149717. hal-05139361

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Research paper

# Texture features of *T2w* magnetic resonance imaging mirror tissue changes in Parkinson's disease models

Chirine Katrib<sup>a</sup>, Camille Naidji<sup>a</sup>, David Devos<sup>a</sup>, Kelly Timmerman<sup>a</sup>, Nicolas Durieux<sup>b</sup>, Nathalie Dutheil<sup>c</sup>, Erwan Bezard<sup>c</sup>, Charlotte Laloux<sup>a,b</sup>, Nacim Betrouni<sup>a,*</sup>

<figure><img src="image_2.png" /></figure>

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## ARTICLE INFO

Edited by Mychael Lourenco.

**Keywords:**

Parkinson's disease

Texture analysis

Magnetic resonance imaging

Toxin-based models

MPTP model

6-OHDA model

Alpha-synuclein-based model

Anatomo-radiological correlations

## ABSTRACT

After successful applications in the oncology field to provide new *in vivo* diagnosis and prognosis imaging features, texture analysis and more generally radiomics were also reported as having the potential to provide markers of different neurodegenerative processes. Indeed, in neurodegenerative diseases such as Parkinson's disease (PD), there is a need for neuroprotective therapies, the development of which will be fundamentally aided by imaging biomarkers capable of inferring tissue changes such as loss of neurons in the nigro-striatal pathway or alpha synuclein aggregates that characterize PD. In this study, we therefore sought to decipher the relationship between signal changes measured using brain MRI texture features and histological changes in preclinical models of this disease.

Three rodent models were used: two toxin-based models, one involving 6-hydroxydopamine injection and the other using methyl-phenyl-tetrahydropyridine, and a third model based on alpha-synuclein overexpression. Animals had MR imaging with a *T2w* sequence evaluation and were sacrificed for histological analyses of the brains. Texture features were measured in different brain structures. The association analyses revealed significant correlations between the imaging features measured in the substantia nigra and the striatum with dopaminergic degeneration, as well as significant correlations between texture features in key structures (substantia nigra, striatum, thalamus, hippocampus and associative and cingulate cortices), and alpha-synuclein quantified in these regions.

These preliminary results suggest that MR signal changes captured using texture features reflect the underlying tissue changes occurring in the brain such as neuronal death and proteins accumulation.

## 1. Introduction

The major pathological hallmarks of Parkinson's disease (PD) include, mainly, dopaminergic neuronal degeneration in the nigrostriatal pathway, abnormal iron build-up and alpha-synuclein accumulation forming Lewy body inclusions (Riederer et al., 2023). With no yet neuroprotective treatment available, the early and predictive biomarkers of these pathological mechanisms are key elements to facilitate candidate drug evaluation (Devos et al., 2022). Over the last few decades, structural, functional and molecular neuroimaging studies have provided invaluable insights into the mechanisms underlying PD (Bidesi et al., 2021). Among these modalities, magnetic resonance imaging

(MRI) with its different exploration methods appeared to be a particularly promising modality. While functional sequences allow extracting quantitative measures as iron load using susceptibility imaging or water diffusion using diffusion tensor imaging (DTI), morphological sequence obtained using T1 weighting, is mainly used to quantify brain structures volumes or shapes. However, it was reported that atrophies or deformation captured using these methods are those appearing at the late diseases stages and the do not have the required sensitivity to detect the slight changes occurring in the early stages.

Another analysis method was described as potentially more sensitive to detect these early changes. Texture analysis is a method that permits to extract statistical features that describe the signal distribution and

<footnote><sup>a</sup> Univ. Lille, INSERM, UMR1172 - Lille Neuroscience & Cognition, F-59000 Lille, France</footnote>

<footnote><sup>b</sup> Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UAR2014-US41 - Lille In Vivo Imaging and Functional Exploration Platform, F-59000 Lille, France</footnote>

<footnote><sup>c</sup> Univ. Bordeaux, CNRS, IMN, UMR 5293, F-33000 Bordeaux, France</footnote>

<footnote>* Corresponding author at: Lille University Hospital- INSERM U1172 - Lille Neuroscience & Cognition Laboratoire de Pharmacologie, Faculté de Médecine 1, Place de Verdun, 59000 Lille, France.</footnote>

<footnote>E-mail address: nacim.betrouni@inserm.fr (N. Betrouni).</footnote>
variation in a defined image region. The assumption is that some of the measured texture features capture changes related to the disease progression and thus can be clinically meaningful (Sørensen et al., 2016). In PD, a 2-year follow-up using T2 weighted images (Sikiö et al., 2015), reported that these features were related to clinical scores of PD severity. The Unified Parkinson's Disease Rating Scale (UPDRS) I score evaluating cognition, behaviour and mood, correlated with texture features extracted from the posterior corona radiata and the substantia nigra. UPDRS-II which scores the self-evaluation of the activities of daily living, correlated with texture features from the putamen, the dentate nucleus and the thalamus, while UPDRS-III score evaluating motor skills, correlated with features in the basilar pons area.

In our previous clinical investigations, we have demonstrated that texture features, measured on morphologic *T1w* images, outperform volumetry to detect slight changes related to both motor and non-motor aspects of the disease. In Betrouni et al. (Betrouni et al., 2020), we showed that texture features measured in the hippocampus, the thalamus and the amygdala were different between patients cognitively intact, patients with slight cognitive slowing and patients with mild cognitive impairment; while in (Betrouni et al., 2021), we showed that texture features from the nigrostriatal pathway accurately separate early-stage PD patients from late-stage patients and correlate with motor handicap.

This study aims to decipher, using images from preclinical models the relationship between the signal changes measured using texture features and the tissular modifications. The study focuses on the *T2w* images, classically used as reference morphological imaging in rodents. To take into account, the variability and the fact that animal modelling allows only to mimic some aspects of the disease, we compiled results from three widely used models, based on different physiological modelling.

# 2. Material and methods

## 2.1. Modelling

The current study was designed as an ancillary investigation for two ongoing studies, the first aiming to compare two toxin-based models of Parkinson's disease and the second focusing on the MR imaging characterization of an alpha-synuclein model. Experiments were carried out following institutional and national guidelines for the care and use of laboratory animals. Applied procedures were approved by the regional and French ethical committees (Authorization number 6281-20 16072517437766 and 33933-2021102916444252).

For the three models, bilateral injections in the substantia-nigra (SN) were used for the lesioning. This modelling was preferred to unilateral lesioning to get close to clinical conditions and to limit the impact of potential compensatory mechanisms.

All the interventions were performed under complete general anaesthesia with ketamine (100 mg/kg) and xylazine (5 mg/kg). Anaesthesia was induced using intra-peritoneal injections.

### 2.1.1. Toxin-based models

Toxin-based models of PD are known to exhibit many signs of oxidative stress and neuroinflammation. Within this category, we chose the 6-hydroxydopamine (6-OHDA) and the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) models. The 6-OHDA rat model is the most widely used and studied model of nigrostriatal lesions, with a highly reproducible dopaminergic degeneration (Betarbet et al., 2002). The neurotoxin is most commonly injected unilaterally in the substantia nigra, medial forebrain bundle, or striatum. It induces a significant dopaminergic loss, ranging from 50 % to 90 %, as well as an important inflammatory response with microglial activation.

The MPTP rat model is much less studied in literature. It is a good representative of the early phase of PD, particularly for the study of non-motor symptoms (Da Cunha et al., 2002). MPTP animals have reduced

dopamine denervation compared to 6-OHDA models (less than 60 % loss), which translates in only mild motor impairments.

Adult Wistar rats were used for these two models. The animals were randomly divided into three groups: a 6-OHDA group (n = 9), an MPTP group (n = 10) and a control group (n = 8) (CTRL-Toxin). By using the procedure already described by (Ferro et al., 2005), the animals received bilateral intranigral injections (Anteroposterior: -5.2, mediolateral: ± 2.2, dorsoventral: -7.8 mm from bregma) of either 6-OHDA (H4381, Sigma) 6 µg/2µl in 0.2 % ascorbic acid (6-OHDA group), or MPTP (M0896, Sigma) 200 µg/2µl (MPTP group), or 2 µl of saline (CTRL-Toxin group).

### 2.1.2. Alpha-synuclein model

The alpha-synuclein model relies on the overexpression of alpha-synuclein by adeno-associated virus (AAV) injection in the substantia nigra. This model has shown a progressive dopaminergic denervation in time with alpha-synuclein expression and propagation (Bourdenx et al., 2015).

Sprague Dawley adult male rats (n = 19, 6 weeks old, 180-200 g) were randomly assigned to an α-synuclein group (ASYN) (n = 13) or a control group (CTRL ASYN) (n = 6). Rats received two bilateral intranigral injections of 1 µL at 250 nL/min (Anteroposterior: -4.9 and -5.4, mediolateral: ± 2.1, dorsoventral: -7.8, in mm from bregma) of a recombinant AAV2/9-p.A53T-human-α-syn (7 x 10¹² gcp/ml) for the ASYN group, or AAV2/9-GFP (7 x 10¹² gcp/ml) for the CTRL group (Institut des Maladies Neurodegeneratives UMR 5293, Bordeaux, France).

## 2.2. Imaging and processing

The MPTP and 6OHDA toxins are known to have a time-limited effect, unlike the synuclein-based model. Therefore, in toxin-based models, the MR imaging exploration was carried out 2 months post-injection while in the alpha-synuclein model, it was done 4 months post-injection. The animals were subsequently sacrificed for brain histological assessments.

MRI proton imaging experiments were performed at 7 Tesla in a horizontal bore magnet (Bruker, Biospec®, Ettlingen, Germany). Animals were anaesthetized with isoflurane (2 %), and their temperature and breathing rate were monitored during the experiments. Structural T2 weighted MR sequence was acquired in Turbo RARE (Rapid Acquisition with Relaxation Enhancement) mode, with the following parameters: spin-echo sequence: TR/TE = 3000/33 ms, field of view (FOV) = 4 cm², voxel size = 0.15x0.15x0.75 mm³).

The original *T2w* images were first corrected for intensity non-uniformity, caused by static field inhomogeneity, using the N3 algorithm (Sled et al. 1998) and then spatially registered to an anatomic atlas: the Wistar atlas (Valdés-Hernández et al., 2011) for toxin-based model and the Waxholm Space Atlas of the Sprague Dawley Rat Brain (Papp et al., 2014), to accurately define regions of interest (Fig. 1).

### 2.2.1. Volumetry

Using the anatomical delineations of the atlases and by assuming their supposed involvement in the disease's development and progression, different structures were considered: the substantia nigra and striatum for all the animals, and for the ASYN group: thalamus, hippocampus, associative cortex and cingulate cortex to evaluate alpha-synuclein spreading.

The corresponding volumes were measured and normalized to the total intra-cranial volume of each rat.

### 2.2.2. Texture analysis

There are dozens of texture parameters. Most of them correlate well with each other. In our previous studies, we focused on first-order parameters and second-order parameters of order, we assume that they allow capturing most of signal variation. The first-order statistics are
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** A) Sprague Dawley Rat Brain atlas: anatomical image and masks of the brain structures. B) Wistar Atlas: anatomical image and masks of the brain structures. C) *T2w* image of a 6-OHDA rat. D) *T2w* image of an MPTP rat. E) *T2w* image of an alpha-syn rat.</figcaption></figure>

used to study the voxel value distribution without considering spatial relationships while second-order statistics describe relationships between pairs of neighbouring voxels in the image, therefore quantifying the spatial relationships between the values.

In that respect, 10 texture were considered and computed in each ROI. Four first-order including Mean grey level; Standard deviation (SD) of grey levels; Kurtosis, which is a measure of whether the data is heavy-tailed or light-tailed in relation to a normal distribution; and Skewness, which also quantifies the asymmetry of signal values in relation to a normal distribution. The second-order statistics are mainly computed through the grey level co-occurrence matrix (GLCM) introduced by Haralick et al. in 1973. Second-order texture values were derived from the co-occurrence matrix whose entries represent the number of times that intensity levels i and j occur in two voxels separated by a distance (d) in a specific direction (a). To obtain a full neighbourhood interaction, a matrix was selected to cover 26 connected directions of neighbouring voxels in a 3D space. Seven parameters were considered: Homogeneity (also called angular second moment), which represents uniformity of the texture intensity; Contrast, representing the degree to which the texture intensity levels differ between voxels or local intensity variation; Entropy, (measure of randomness), which represents the degree of uncertainty; Correlation, representing the degree of mutual dependency between voxels; Variance (also called sum of squares), which gives high weights for elements different from the average value; Sum average, measuring the relationship between occurrences of pairs with lower intensity values and occurrences of pairs with higher intensity values; and Inverse difference moment (InvDiff), which measures the difference between the highest and lowest values of a contiguous set of voxels. (Complete description of the computation of the texture features

can be found in (Betrouni et al., 2021; 2020).

The texture features were computed on the delineated ROIs of both brain hemispheres, and the left and right sides values were averaged to obtain global values representative of the structure.

Implementations and computations were done using a home-made code written in Matlab.

## 2.3. Histological assessment

### 2.3.1. Dopaminergic degeneration

Dopaminergic denervation quantification was done for all the animals included in this study. It was assessed by tyrosine hydroxylase (TH) immunostaining of striatal and nigral sections. The brain sections were incubated with 0.3 % hydrogen peroxide in a 0.1 phosphate buffer, pH 7.4 (PBS), to quench endogenous peroxidase activity. Then they were incubated in a 0.3 % Triton X-100 PBS blocking solution with 10 % normal serum. A primary antibody anti-TH 1:1000 (AB152 Sigma-Aldrich), then a biotinylated secondary antibody goat anti-rabbit (BA1000, VectorLabs) were used. Antibody binding was detected with the avidin-biotin-peroxidase conjugate (Vectastain ABC Elite; Vector-Labs) using 3,3'-diaminobenzidine as the chromogen. Finally, sections were dehydrated through graded ethanol solutions, cleared in xylene, and mounted in DPX (DBH Laboratory Supplies).

Images were acquired using a Zeiss Axio Scan Z.1 slide scanner (20 × objective) and Zen software, Blue edition (Zeiss, Oberkochen, Germany). Zen Software measured the optical density of TH-ir terminals in the striatum. The number of TH-ir neurons in the substantia nigra was counted by unbiased stereology using Mercator Explora Nova Software, with an optical fractionator method to allow an accurate and efficient estimate of labelled neuron number as previously described (Laloux et al., 2017).

### 2.3.2. Alpha-synuclein staining

The same immunohistochemistry protocol described above was used for α-synuclein staining, with primary antibody α-synuclein mouse mAb, clone LB509 (180215 Invitrogen) for human α-synuclein (h-α-syn), and secondary antibody horse anti-mouse (BA-2000, VectorLabs). These experiments were run only on the 13 rats of the ASYN group.

After axoscan acquisition, LB509-unravelled α-synuclein was assessed using a custom 4-point scale range from 0 to 3, quantifying the presence and amount of α-synuclein staining (adapted from Joppe and colleagues, 2021): 0 = absence of alpha-synuclein strings; 1 = few alpha-synuclein strings (Mild); 2 = moderate presence of alpha-synuclein strings; 3: Dense alpha-synuclein strings (Fig. 2).

Using the QuickNII spatial registration software (Yates et al., 2019), each region of interest among those described above, was automatically delineated from the Waxholm Space Atlas of the Sprague Dawley Rat Brain (Papp et al., 2014) and was scored allowing, thus to obtain an average score of α-synuclein staining per region.

Complete description of this quantification can be found in (Katrib et al., 2024).

## 2.4. Statistical analysis

For group comparisons, we considered each model group against its control group, even in the toxin-based models where the study was designed as a three-group study. Thus, the 6-OHDA group was compared to the CTRL-Toxin, the MPTP group to the CTRL-Toxin and the ASYN group to the CTRL-ASYN.

### 2.4.1. Histological scores

All data are expressed as mean ± SEM or as percentage of the CTRL group. Scores were compared using Mann-Whitney tests.

### 2.4.2. Imaging features

Volumes of the brain structures defined in the methods section were
<figure><img src="image_4.png" /><figcaption>Fig. 2. A. The alpha-synuclein quantification 4-level scale used for analysis. It was defined for each region based on the number/density of α-syn strings.</figcaption></figure>

compared between the groups using Mann-Whitney tests.

Regarding the texture features, first they were compared between the groups using Mann-Whitney tests. The association between the texture features and the neural density was examined using Spearman's correlation, measured between the imaging features in the substantia nigra and the striatum, and the number of neurons or density of neuronal terminals respectively in these structures in both animal groups; while the association between the imaging features and the alpha-synuclein concentration was examined only by considering the ASYN group and using the Spearman's correlation, measured between the texture features in the brain structures indicated above and the corresponding alpha-synuclein distribution scores.

For all the analyses, significance was fixed at p-value < 0.05. False discovery rate (FDR) correction for multiple testing was applied to correct the significance for each case.

## 3. Results

### 3.1. Histological assessment

Assessment of TH immunoreactivity in the nigrostriatal pathway revealed significant reduction of dopaminergic TH-ir neurons in the substantia nigra pars compacta (SNpc) and of dopaminergic terminals in the striatum, in the three models' groups when compared to their respective controls. In the 6-OHDA group, the percentage of dopamine TH-ir neuron loss was evaluated at an average of 45% (p = 0.001) in the SNpc and 47% (p < 0.001) in the striatum. In the MPTP, scores were 36% (p = 0.012) and 43% (p = 0.001) in the striatum respectively. Lastly, in the ASYN group, loss was 47% in the SNpc (p < 0.001) and 27% (p = 0.02) in the striatum respectively. Additionally, in this group, alpha-synuclein staining was observed both anterior and posterior to the injection site, with the protein reaching the olfactory bulb on one side and

the brainstem on the other. The main regions with moderate to severe α-synuclein pathology include the caudate-putamen, the substantia nigra pars compacta, the thalamus, and the hippocampus. Several cortical areas also presented with moderate to severe α-synuclein scores, mainly the cingulate and association cortices (Table 1).

### 3.2. Imaging features

Volume comparisons between the groups revealed no statistically significant differences in any of the considered brain structures. Regarding texture features, the tests showed differences. In the 6-OHDA group, differences were found for standard-deviation (p = 0.007) and kurtosis (p = 0.01). In the MPTP group, differences were mainly found for standard-deviation (p = 0.001), skewness (p = 0.005) and entropy (p = 0.01). In the ASYN group, differences were found for two texture features: standard-deviation (p = 0.001) and entropy (p = 0.004) in the SN, and standard-deviation (p = 0.01) and kurtosis (p = 0.009) in the striatum.

### 3.3. Texture features and neurodegeneration

In the two toxin-based models, while no statistically significant correlations were found in the striatum; in the SN, different texture features exhibited significant correlations with the TH-ir neuronal density. The main results are summarised in Table 2.

In the ASYN group, the correlation analysis (n = 13 rats) showed a clear association between the first-order statistics texture feature skewness in both the substantia nigra and the striatum and the corresponding TH-ir neuronal density. While in the striatum, the kurtosis features also showed a statistically significant correlation with the measured TH-ir terminals density (in the striatum, skewness r = -0.55, p = 0.001, kurtosis r = -0.48, p = 0.009, in the substantia nigra

**Table 1**

Mean values of the alpha-synuclein scoring in the regions of interest used for the correlations analysis with corresponding MR T2w texture features (n = 13 rats in the alpha-syn group). LB509-unravelled α-synuclein labelling using a custom 4-point scale range from 0 to 3 (0 = absence of alpha-synuclein strings; 1 = few alpha-synuclein strings; 2 = moderate presence of alpha-synuclein strings; 3: Dense alpha-synuclein strings).

<table><thead><tr><th>Rat</th><th>Substantia-nigra</th><th>Striatum</th><th>Thalamus</th><th>Hippocampus</th><th>Associative cortex</th><th>Cingulate cortex</th></tr></thead><tbody><tr><td>1</td><td>2.5</td><td>2.5</td><td>2.5</td><td>2</td><td>1.25</td><td>0.5</td></tr><tr><td>2</td><td>1.25</td><td>1.75</td><td>1</td><td>3</td><td>2.5</td><td>0.25</td></tr><tr><td>3</td><td>2.75</td><td>2.5</td><td>1.5</td><td>0</td><td>1.75</td><td>1.25</td></tr><tr><td>4</td><td>2.25</td><td>2.5</td><td>1.5</td><td>1.25</td><td>2.25</td><td>1.75</td></tr><tr><td>5</td><td>1.5</td><td>2.5</td><td>1</td><td>0</td><td>1</td><td>0.5</td></tr><tr><td>6</td><td>2.5</td><td>2.25</td><td>0.75</td><td>2</td><td>1.5</td><td>0.5</td></tr><tr><td>7</td><td>0.25</td><td>0.5</td><td>1.25</td><td>3</td><td>0</td><td>0</td></tr><tr><td>8</td><td>2</td><td>2.5</td><td>0.5</td><td>0.25</td><td>2.25</td><td>1.5</td></tr><tr><td>9</td><td>2.75</td><td>2.5</td><td>0</td><td>0</td><td>1.5</td><td>1.5</td></tr><tr><td>10</td><td>2.5</td><td>3</td><td>3</td><td>2</td><td>2.5</td><td>1.5</td></tr><tr><td>11</td><td>1.5</td><td>3</td><td>2</td><td>2</td><td>2</td><td>2</td></tr><tr><td>12</td><td>2.5</td><td>2.5</td><td>1</td><td>1</td><td>2</td><td>2.5</td></tr><tr><td>13</td><td>2</td><td>3</td><td>0</td><td>0</td><td>2</td><td>1</td></tr><tr><td>Mean</td><td>2</td><td>2.4</td><td>1.2</td><td>1.3</td><td>1.7</td><td>1.1</td></tr></tbody></table>
<caption>**Table 2**</caption>

Spearman's correlation between percentage of TH-ir neurons in the substantia-nigra and texture features measured in this structure using T2w MR images in the toxin-based groups (n = 9 rats in the 6-OHDA group and n = 10 in the MPTP group).

<table><thead><tr><th>Texture feature</th><th>Statistics</th></tr></thead><tbody><tr><td>Standard-deviation</td><td>r = 0.60, p = 0.001</td></tr><tr><td>Variance</td><td>r = -0.42, p = 0.03</td></tr><tr><td>Contrast</td><td>r = -0.41, p = 0.03</td></tr><tr><td>Correlation</td><td>r = 0.47, p = 0.01</td></tr><tr><td>Inverse difference moment</td><td>r = -0.59, p = 0.001</td></tr><tr><td>Entropy</td><td>r = -0.60, p = 0.001</td></tr></tbody></table>

skewness r = -0.59, p = 0.001, Fig. 3).

### 3.4. Texture features and alpha-synuclein overexpression

Associations between texture features and alpha-synuclein over-expression in different brain structures of the animal from the ASYN group showed statistically significant correlations. Table 3 summarises the main findings:

## 4. Discussion

### 4.1. Main findings

The results reported herein constitute the first application of MR image texture analysis in animal models of Parkinson's disease. The application of this post-processing method, on in vivo structural T2w images has yielded interesting correlative patterns. Indeed, these ana-lyses allowed to make correlations with histopathological scores, hence suggesting that signal changes captured using these imaging features reflect the underlying tissue changes occurring in the brain, mainly neuronal death and protein accumulation. In the three preclinical models, texture features in the nigrostriatal pathway mirrored the dopaminergic degeneration which was observed in the substantia nigra and the striatum, as shown in the histology measurements. This confirms that ROI-based texture analysis is a sensitive image analysis technique

<caption>**Table 3**</caption>

Spearman's correlation between alpha-synuclein overexpression and texture features in different brain structures. (n = 13 rats in the alpha-syn group).

<table><thead><tr><th>Brain structure</th><th>Texture feature</th><th>Statistics</th></tr></thead><tbody><tr><td rowspan="3">Substantia nigra</td><td>Standard-deviation</td><td>r = 0.81, p = 0.001</td></tr><tr><td>Inverse difference moment</td><td>r = -0.60, p = 0.01</td></tr><tr><td>Entropy</td><td>r = -0.58, p = 0.02</td></tr><tr><td rowspan="3">Striatum</td><td>Kurtosis</td><td>r = 0.62, p = 0.002</td></tr><tr><td>Homogeneity</td><td>r = -0.58, p = 0.006</td></tr><tr><td>Inverse difference moment</td><td>r = -0.63, p = 0.002</td></tr><tr><td rowspan="3">Thalamus</td><td>Entropy</td><td>r = -0.63, p = 0.002</td></tr><tr><td>Contrast</td><td>r = -0.70, p = 0.005</td></tr><tr><td>Standard-deviation</td><td>r = -0.62, p = 0.01</td></tr><tr><td rowspan="3">Hippocampus</td><td>Inverse difference moment</td><td>r = 0.63, p = 0.01</td></tr><tr><td>Mean gray level</td><td>r = -0.70, p = 0.009</td></tr><tr><td>Sum-Average</td><td>r = -0.65, p = 0.01</td></tr><tr><td rowspan="3">Associative cortex</td><td>Contrast</td><td>r = -0.60, p = 0.01</td></tr><tr><td>Variance</td><td>r = -0.67, p = 0.009</td></tr><tr><td>Skewness</td><td>r = 0.64, p = 0.002</td></tr><tr><td rowspan="2">Cingulate cortex</td><td>Standard-deviation</td><td>r = 0.60, p = 0.01</td></tr><tr><td>Skewness</td><td>r = 0.65, p = 0.01</td></tr></tbody></table>

that could detect very subtle changes in the signal, in this case, when the neurodegeneration has not yet reached a critical point leading to major morphological changes in the structure. In a clinical context, patients meet the criteria for PD only when 60–70 % of the neurons in the sub-stantia nigra have degenerated and approximately 80 % of the striatal dopamine content has been lost (Riederer et al., 2023).

In the three models considered, dopaminergic degeneration was lower than 50 % in both the SN and the striatum. While volumetry analysis was unable to highlight differences between the target groups and the control groups, texture features, in addition of being correlated with tissue changes, showed differences between these groups, as already demonstrated using clinical images (Betrouni et al., 2021; 2020). These results show the translational aspect of this methodology.

### 4.2. Signal kurtosis and skewness capture dopaminergic degeneration

Two main texture features from first-order statistics, the kurtosis and

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Spearman's correlation of percentage of TH-ir neurons in the substantia-nigra and TH-ir terminals density in the striatum with texture features in these two regions. (n = 13 rats in the alpha-syn group).</figcaption></figure>

<figure><img src="image_7.png" /></figure>
the skewness, showed correlations with the dopaminergic neuronal labelling in the striatum and the substantia nigra. These two features that globally inform about the signal deviation regarding the normal distribution are particularly sensitive to the signal changes induced by neural density alterations. In previous investigations of this method using clinical data, these features were reported as more sensitive than volumetry to discriminate PD groups with different disease stages (Betrouni et al., 2021; 2020, Devignes et al., 2021). Furthermore, (Colgan et al., 2017), carried out an imaging-histological analysis using a preclinical model of Alzheimer's disease and observed significant correlations between tau burden and texture features, mainly skewness in the hippocampus and thalamus, confirming that these features can reflect brain tissue changes in different contexts.

### 4.3. Texture feature inform about alpha-synuclein spreading

The other main outcome of this study is the association between the LB509 immunopositivity for α-synuclein and different texture features measured in key structures from the nigrostriatal pathway, the cortex, etc... (Table 2). These results suggest that alpha-synuclein changes alter the MR signal, and texture features can capture the resulting patterns. The features that yielded significant results included both first- and second-order statistics, such as inverse difference moment and entropy. The main difference between these two categories of features is whether or not the neighbourhood is considered. Second-order statistic features provide an information regarding the relationship between the voxels, therefore better demonstrate the intra-ROI heterogeneity. By definition, texture features, either from first or second order statistics, are strongly correlated between them, but bring complementary information about the global and local signal changes in a region. In the oncology field where the first effective clinical applications of texture analysis were reported for therapy prognosis (mainly radiotherapy and chemotherapy), these features are usually combined using machine learning methods to build the models (Aerts et al., 2014).

Aggregated α-synuclein, as an early driver of α-synucleopathy pathogenesis, could serve as a suitable imaging biomarker, particularly in Parkinson's disease. However, to date, there is no imaging biomarker for a definitive early diagnosis or prognosis. Nuclear medicine modalities (single-photon emission computed tomography and positron emission tomography) allow only the detection of dopaminergic deficits but not the quantification of the protein as is the case in Alzheimer's disease, with radiotracers having the required sensitivity for tau or beta-amyloid plaques quantification (Alzghool et al., 2022). Efforts to develop these tracers are still underway, but until we have direct measurement methods, indirect measurement using MRI could be a solution.

### 4.4. Multimodal combination for models optimisation

Texture analysis allows extracting quantitative features from morphologic MR images, that can replace standard volumetry and morphometry parameters and, as demonstrated in the current work but in our previous clinical investigations, outperform them in terms of sensitivity to detect early changes. In this way, they can be candidate for a combination with other parameters from MR quantitative sequences, such as R2* or DTI values, to build diagnosis and prognosis models.

### 4.5. Limits and futures work

As conducted in the present work, the experiments suffer from a limit, it concerns the scale used to evaluate α-synuclein. While inspired by clinical practices that utilize various semi-quantitative scales to assess the deficits severity, it remains insufficient for precise quantitative evaluation. The low intensity of α-synuclein staining precluded automated processing to obtain full quantitative values. Correlation analyses would be more interesting with these values. It would therefore be important to confirm these results.

The second limit is the lack of longitudinal follow-up to study the evolution of texture features with the disease progression. A study with different time points in the three models could be a solution, but it would be challenging and require a large number of animals to be used.

## 5. Conclusion

In 2016, Robert Gillies (Gillies et al., 2016) wrote: "Images Are More than Pictures, They Are Data". But they can only become data, and to a certain extent, possible biomarkers, when we find a way to interpret the underlying anatomical, cellular and molecular changes that are reflected in images. This process remains a challenge, especially in neurology, because unlike the oncology field, where we can readily have biopsies of the tissue, the brain's histological signature remains a mystery and only the association of clinical cohorts with brain biobanks could afford this kind of analysis at the human level.

### CRediT authorship contribution statement

**Chirine Katrib:** Writing – original draft, Visualization, Methodology, Investigation, Formal analysis. **Camille Naidji:** Visualization, Formal analysis. **David Devos:** Writing – review & editing, Resources. **Kelly Timmerman:** Writing – review & editing, Visualization, Methodology. **Nicolas Durieux:** Writing – review & editing, Methodology. **Nathalie Dutheil:** Writing – review & editing, Resources. **Erwan Bezard:** Writing – review & editing, Resources. **Charlotte Laloux:** Writing – review & editing, Supervision, Project administration, Investigation, Funding acquisition. **Nacim Betrouni:** Writing – review & editing, Supervision, Resources, Project administration, Formal analysis, Conceptualization.

### Declaration of competing interest

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.

### Acknowledgement

This work was supported by a grant from the charity association France Parkinson.

### Data availability

Data used in this study can be requested from the corresponding author or freely downloaded from: https://data.mendeley.com/data-sets/y937ggjgvt/1.

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