# Brain glucose metabolism as a neuronal substrate of the abnormal behavioral response to stress in the mdx mouse, a model of Duchenne muscular dystrophy

Sébastien Goutal, Marion Lancien, Francois Rivier, Nicolas Tournier, Cyrille

Vaillend

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Sébastien Goutal, Marion Lancien, Francois Rivier, Nicolas Tournier, Cyrille Vaillend. Brain glucose metabolism as a neuronal substrate of the abnormal behavioral response to stress in the mdx mouse, a model of Duchenne muscular dystrophy. Neurobiology of Disease, 2025, 204, pp.106771. 10.1016/j.nbd.2024.106771. hal-04848520

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Distributed under a Creative Commons Attribution 4.0 International License
Brain glucose metabolism as a neuronal substrate of the
abnormal behavioral response to stress in the mdx mouse,
a model of Duchenne muscular dystrophy

Sébastien Goutal, Marion Lancien, François Rivier, Nicolas Tournier, Cyrille Vaillend

¹ Université Paris-Saclay, INSERM, CNRS, CEA, Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Service Hospitalier Frédéric Joliot, 91401 Orsay, France. Electronic address: sebastien.goutal@universite-paris-saclay.fr

² Université Paris-Saclay, CNRS, Institut des Neurosciences Paris Saclay, 91400 Saclay, France; PhyMedExp, CNRS UMR 9214, INSERM U1046, University of Montpellier, CHU de Montpellier, France. Electronic address: m-lancien@chu-montpellier.fr

³ PhyMedExp, CNRS UMR 9214, INSERM U1046, University of Montpellier, CHU de Montpellier, France. Electronic address: f-rivier@chu-montpellier.fr

⁴ Université Paris-Saclay, INSERM, CNRS, CEA, Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Service Hospitalier Frédéric Joliot, 91401 Orsay, France. Electronic address: nicolas.tournier@cea.fr

⁵ Université Paris-Saclay, CNRS, Institut des Neurosciences Paris Saclay, 91400 Saclay, France. Electronic address: cyrille.vaillend@universite-paris-saclay.fr

**Keywords:**

Duchenne muscular dystrophy; Dystrophin; Fear circuit; Functional brain imaging; Hypothalamus; Unconditioned fear.
Abstract

Duchenne muscular dystrophy (DMD) is associated with a range of cognitive and behavioral problems. Brain-related comorbidities show clinical heterogeneity depending on the position of the mutation within the multi-promoter dystrophin (DMD) gene, likely due to the differential impact of mutations on the expression of distinct brain dystrophins. A deficiency of the full-length brain dystrophin, Dp427, has been associated with enhanced stress reactivity, characterized by abnormal fear responses in both patients and mdx mouse model. However, the neural substrates of this phenotype are still unknown. Here, we undertook the first functional imaging study of the mdx mouse brain, following expression of the typical unconditioned fear response expressed by mdx mice after a short scruff restraint and one week later after recovery from stress. We compared the brain glucose metabolism in 12 brain structures of mdx and WT littermate male mice using [¹⁸F]FDG PET imaging. Restraint-stress induced a global decrease in [¹⁸F]FDG uptake in mdx mice, while no difference was found between genotypes when mice were tested one week later under non-stressful conditions. A subset of brain structures were particularly affected by stress in mdx mice, and we identified abnormal correlations between fear responses and metabolism in specific structures, and altered co-activation of the hypothalamus with several subcortical structures. Our data support the hypothesis that enhanced stress reactivity due to loss of brain Dp427 relies on abnormal activation of the brain fear circuit and deregulation of a hypothalamus-dependent pathway.
INTRODUCTION

Duchenne muscular dystrophy (DMD) is an X-linked recessive neuromuscular syndrome that affects 1 out of 5000 male births (Crisafulli et al., 2020). It is characterized by progressive muscular weakness leading to loss of mobility, cardiomyopathy, and respiratory failure (Ryder et al., 2017). In addition to this predominant muscular phenotype, patients with DMD frequently have mild to severe cognitive impairments (Colombo et al., 2017) and a higher prevalence of neuropsychiatric disorders than the general population, which may include autism spectrum disorder, attention deficits, and emotional disturbances (Banihani et al., 2015; Hinton et al., 2009; Pascual-Morena et al., 2022; Ricotti et al., 2016).

DMD is caused by pathogenic variants of the *DMD* gene that prevent the expression of dystrophin proteins (Koenig et al., 1987). Dystrophins are core components of transmembrane dystrophin-associated glycoprotein complexes that link the actin cytoskeleton to extracellular matrix proteins in various cell types (Ervasti and Campbell, 1993). The *DMD* gene contains several independent promoters enabling the production of several dystrophin isoforms of distinct sizes that share a common C-terminus (Muntoni et al., 2003) and are expressed in a cell and tissue-specific manner (Perronnet and Vaillend, 2010). The full-length dystrophin protein called Dp427 (427 kDa), as well as the Dp140 (140 kDa) and Dp71 (71kDa) isoforms, are all expressed in the brain (Hugnot et al., 1992; Lidov et al., 1995, 1990), and their absence underlies the cognitive and behavioral disturbances associated with DMD. The loss of Dp427 in both muscle and brain is common to all DMD patients regardless of their mutation position. It has been associated with mild cognitive deficits and emotional problems. More downstream mutations, leading to the additional loss of Dp140 and Dp71, are associated with more severe cognitive and behavioral deficits (Colombo et al., 2017; Ricotti et al., 2016; Taylor et al., 2010). Emotional problems are reported in all subgroups of patients including those with a selective deficiency in Dp427, and include both internalizing problems (anxiety, depression) and externalizing problems (aggressiveness, hyperactivity) (Ricotti et al., 2016). A recent study particularly pointed to abnormally enhanced unconditioned skin conductance and bradycardia in DMD patients in response to a threat, suggesting that the unconditioned startle response is a relevant biomarker of DMD central defects (Maesh et al., 2022).

Studies in DMD mouse models holding distinct mutations within the *dmd* gene confirmed that emotional disturbances represent a main endophenotype associated with the loss of brain dystrophins (Saoudi et al., 2021, 2021; Vaillend and Chaussenot, 2017; Vaillend and Ungerer, 1999). In *mdx* mice that selectively lack Dp427 due to a point mutation leading to a STOP codon in exon 23 of the *dmd* gene (Sicinski et al., 1989), the behavioral phenotype shows similarities with the neuropsychological profile of DMD patients. *mdx* mice display mild memory deficits (Vaillend et al., 2004), anxiety, depressive-like behaviors, reduced exploratory behavior, and abnormal conditioned and unconditioned fear responses (Chaussenot et al., 2015; Sekiguchi et al., 2009; Vaillend et al., 1995; Vaillend and Chaussenot, 2017). The abnormal unconditioned fear, characterized in these studies by long-lasting freezing responses (tonic immobility), was observed after presentation of a variety of mild stressors that do not cause motor inhibition in wild-type mice, such as scruff and tube restraint, suggesting a drastically enhanced stress reactivity in *mdx* mice. The phenotype shows comparable magnitude in distinct mouse models, which confirms a specific contribution of the Dp427 isoform (Saoudi et al., 2021). A key study further showed that severe social-subordination stress induces sustained hypotension, cardiac damage, and a high lethality rate in *mdx* mice (Razzoli et al., 2020). As recently reviewed (Gharibi et al., 2024),
enhanced stress reactivity related to dystrophin dysfunction is highly conserved across animal species and constitutes the most robust and non-habituating phenotype in the *mdx* mouse model. Several studies also highlighted the complex and multifactorial basis of this phenotype, which involves altered central GABAergic inhibition, deregulation of the hypothalamic-pituitary-adrenal and renin-angiotensin-aldosterone systems, as well as autonomic and hormonal dysfunctions.

Importantly, the abnormal fear response in *mdx* mice was reduced by the rescued expression of brain Dp427 following brain delivery of antisense oligonucleotides designed to skip the mutated exon 23, which restored the open reading frame and enabled partial rescue of the Dp427 protein (Goyenvalle et al., 2015; Sekiguchi et al., 2009; Zarrouki et al., 2022b). This indicates that the phenotype has, at least in part, a central origin, resulting from brain Dp427 deficiency. The role of brain Dp427 is not fully understood. This dystrophin isoform has been mostly detected in the cerebellum, hippocampus, amygdala, and cerebral cortex (Doorenweerd et al., 2017b; Lidov et al., 1990; Lidov, 1996; Lidov et al., 1993; Sekiguchi et al., 2009). It is enriched in post-synaptic densities of inhibitory synapses (Kim et al., 1992), contributing to the clustering of GABAA receptors (Knuesel et al., 1999). In *mdx* mice, the loss of Dp427 in the basolateral nucleus of the amygdala is associated with reduced GABAergic inhibitory synaptic currents in response to norepinephrine inputs, suggesting that a dysfunctional fear circuit could be one of the pathways leading to enhanced fear response in *mdx* mice. Abnormal distribution of GABAA receptors has been confirmed in the prefrontal cortex of DMD patients using 123I-IMZ single photon emission computed tomography (Suzuki et al., 2017), and altered clustering was also characterized in the hippocampus, cerebellum, and cortex (Zarrouki et al., 2022a) of the *mdx* mouse. In the hippocampus and cerebellum of *mdx* mice, the resulting reduction in GABAergic inhibition leads to changes in the number and strength of inhibitory synaptic connections and alters synaptic plasticity (Dallérac et al., 2011; Fuenzalida et al., 2016; Krasowska et al., 2014; Kreko-Pierce and Pugh, 2022; Miranda et al., 2011, 2009; Pereira da Silva et al., 2018; Vaillend and Billard, 2002; Wu et al., 2022). Therefore, alterations in the GABAergic system and possible alterations in other systems result in modifications in neuronal circuit development and plasticity, activity, and connectivity, which likely affect a range of brain structures that normally express Dp427.

A first *in vivo* MRI anatomical study (7T, RARE sequences, 50 µm resolution) did not show any anatomical alterations in *mdx* mice compared to WT mice in five studied brain areas (cerebellum, olfactory bulbs, whole cortical area, ventricles, hippocampus) (Miranda et al., 2009). Another study (7 Tesla, FLAIR sequences, 78 µm resolution) found enlarged lateral ventricles in *mdx* mice with no difference in brain tissue volumes, while diffusion tensor imaging revealed elevated diffusivity in the prefrontal cortex and reduced fractional anisotropy in the hippocampus (Xu et al., 2015). A post-mortem MRI study of brains fixed with paraformaldehyde (7T, FLASH sequences, 70 µm resolution) revealed, among 22 measured brain regions, increases in the volumes of the hippocampus, globus pallidus, and caudate putamen. In contrast, the volume of the hypothalamus was decreased (Kogelman et al., 2018). However, a recent *in vivo* study (7 Tesla, RARE sequences, 70 µm resolution) segmenting brain into 23 structures did not detect any volumetric alteration in *mdx* mice (Verhaeg et al., 2024).

Brain morphology has also been studied by *in vivo* imaging in DMD patients: a retrospective MRI and scanner study suggested a frontal atrophy (Mori-Yoshimura et al., 2022), while comparative MRI studies found discrete but significant reductions in grey matter volume (Doorenweerd et al., 2014) and decreased local synchronization of spontaneous activity in the
motor cortex (Lv et al., 2011). However, even with no or discrete anatomical differences, it
may be hypothesized that the function of several cerebral regions could be altered. Two
positron emission tomography (PET) studies using [18F]fluoro-2-deoxy-D-glucose
([18F]FDG) in DMD patients found altered regional glucose metabolism in the brain of DMD
patients, notably in cerebellar, medial temporal and associative cortical areas (Bresolin et al.,
1994) (Lee et al., 2002). Hyperconnectivity in the default mode network has also been
observed in DMD patients using resting-state functional MRI (Doorenweerd et al., 2021).
Early studies also reported the presence of non-specific electroencephalographic (EEG)
abnormalities in DMD patients (Anderson et al., 2002). This suggested synaptic dysfunctions,
which have been confirmed by electrophysiological studies of DMD mouse models
(Perronnet and Vaillend, 2010). More recent cohort studies reported variable prevalence of
epilepsy (1.4-8%) in DMD patients, variably associated with EEG abnormalities (Hendriksen
et al., 2018; Kizek et al., 2024; Pane et al., 2013), suggesting a discrete occurrence that has
not been correlated to specific genotype and cognitive status.

To our knowledge, there has been no functional neuroimaging in *mdx* mice. Apart from the
presumed involvement of the amygdala in the genesis of the enhanced stress reactivity in *mdx*
mice, the neuroanatomical basis of this most robust phenotype is still unknown. In the present
study, we hypothesized metabolic changes could occur in specific brain regions involved in
the processing of fearful stimuli in *mdx* mice. We used brain [18F]FDG PET imaging to
compare brain glucose metabolism in selected brain regions of *mdx* and WT littermate mice,
following exposure to the scruff-restraint stressor and after a one-week post-stress recovery.

MATERIALS AND METHODS

Animals

Mice of the C57BL/10ScSn-Dmdmdx/J (mdx) and C57BL/10ScSn strains were originally
purchased from Charles River Laboratories (France) and Harlan (UK), respectively, and then
bred at Neuro-PSI (Orsay, France). The study was undertaken in male mice because DMD is
an X-linked recessive disorder that selectively affects hemizygous male subjects. The
dystrophin-deficient mdx and wild-type littermate male mice (WT) used in this study were
obtained by mating heterozygous mdx females with C57BL/10ScSn males. Male siblings
were kept in group (3-5 per cage) under a 12-h light-dark cycle (light on: 7.00 a.m.) with food
and water ad libitum. Four litters containing naive male mice of both genotypes (verified by
PCR) were selected and transferred to the imaging center (Orsay, France) at the age of 7-8
months. Mice were allowed 4-5 days of adaptation to their new housing condition before the
start of experiments. Seven WT (34.3 +/-1,7 g) and 7 mdx test mice (36.9 +/-3.9 g) were
pseudo-randomly selected from the 4 litters for the experiments. Animal care and procedures
complied with the European Communities Council Directive (CEE 86/609/EEC), EU
Directive 2010/63/EU, French National Committee (87/848) and ethic committees N°57 and
59, and approved under the reference N° APAFIS#7466.

Experimental design

A dedicated PET protocol was used to enable simultaneous acquisition of behavioral and
brain [18F]FDG PET data in mice (graphical representation in Fig.1 A). In this protocol,
[18F]FDG was injected intraperitoneally (i.p.) in awake mice to allow for (irreversible)
uptake by the brain in freely moving animals while undergoing behavioral assessments (Hugon et al., 2022). Mice underwent two subsequent experimental sessions: First, the “STRESS” condition, and after a one-week post-stress recovery, the “NO STRESS” condition. In each session, the mouse was submitted to a behavioral test immediately after injection of [18F]FDG, either by scruff restraint (STRESS condition) or by tail handling (NO STRESS condition). Test mice were food deprived 12-14h before the session and returned to *ad libitum* feeding after each session. Weight loss was <8%, and mice did not show any sign of pain or distress. Mice were kept in groups among siblings before and after each session (following recovery from anesthesia). Experiments were conducted by one experimenter blind to the genotype.

## Behavioral testing

In the STRESS condition, the unconditioned fear response was induced by manual scruff-restraint stress as previously described (Vaillend and Chaussenot, 2017). This stressor typically triggers a fear response in *mdx* mice, but not in WT mice (Sekiguchi et al., 2009). Briefly, the awake mouse was restrained during 15 s by grasping the scruff between the thumb and index fingers and the back skin and tail with the others fingers, tilting the animal upside-down in order that the ventral part of its body faced the experimenter. [18F]FDG was injected intraperitoneally (i.p.) during this restraint. After 15 s of restraint, the mouse was released to a novel cage (24 x 19cm, with 12-cm high walls) containing clean sawdust, and the locomotor activity of the mouse was recorded for 5 min using the video-tracking Any-maze software (Stoelting, USA).

In the NO STRESS condition, the mouse was gently handled by the tail, and the rear body was lifted while the mouse was holding a grid with its forepaws (Vaillend and Chaussenot, 2017) to allow for i.p. injection of [18F]FDG without any scruff restraint. Mice were then simply released in the novel cage without any prior restraint, and the locomotor activity of the mouse was recorded for 5 min as above.

In both conditions, the fear response was characterized by periods of tonic immobility (freezing) characterized by complete immobilization of the mouse, except for respiration. The time spent freezing was expressed as a percent of the WT level in the STRESS and NO STRESS conditions. After this 5 min recording period, the mouse was transferred into an empty cage, waiting 35 min before being anesthetized and then submitted to PET acquisition 10 min after induction of anesthesia. Thus, the brain uptake of [18F]FDG occurred during 50 min in awake and freely moving mice.

## Brain imaging

[18F]FDG was administered i.p. (8.1±1.4 MBq; 100 µL) 50 min prior to PET acquisition (Schiffer et al., 2007). Forty minutes after the injection (35 min following the 5-min recording of the mouse's behavior), anesthesia was induced using isoflurane 2-2.5% in 100% O2. Anesthetized animals were then rapidly transferred under the microPET scanner (Inveon® microPET system, Siemens, Germany), with the brain in the middle of the field of view. A static 30 min PET acquisition started 50 min after [18F]FDG injection under anesthesia maintained by a facial mask (1.5-2% isoflurane). Static brain PET acquisition was performed at the plateau of [18F]FDG brain kinetics after i.p. injection, assuming the limited impact of isoflurane anesthesia on the time-course of [18F]FDG uptake by the brain at this stage (Fueger et al., 2006; Toyama et al., 2004).
Images were reconstructed with the FORE+OSEM2D algorithm including normalization, attenuation, scatter and random corrections. The spatial resolution of the PET scanner was 1.5 mm (FWHM). Static [18F]FDG PET images were co-registered to mouse brain FDG Mirrione's template (Mirrione et al., 2007) using PMOD software (version 3.8). Predefined brain regions of this template were used to analyze the volumes of interest (VOI) of the following brain structures: Amygdala, striatum, hippocampus, hypothalamus, thalamus, midbrain, cerebellum, basal forebrain, rest of the brainstem, central gray, olfactory bulb, cortex, and whole brain. Radioactivity in each VOI was generated and corrected for 18F decay, injected dose, and animal weight to express the measurements in the standardized uptake value (SUV).

## Statistics and graphical representations

Data are presented as means ± standard deviation (SD). The effect of stress on the fear (freezing) response was analyzed using a two-way analysis of variance (ANOVA) using the genotype as grouping variable (WT, *mdx*) and the emotional state (STRESS vs NO STRESS) as a repeated measure, followed by Sidak's multiple comparisons post-hoc tests. The effect of stress on [18F]FDG PET measurements was first evaluated using a three-way ANOVA using the genotype as grouping variable (WT, *mdx*) and repeated measures (emotional state: STRESS vs NO STRESS; 12 brain structures), followed by two-way ANOVAs to analyze interactions between genotype and emotional state in each brain structure. Homoscedasticity was assessed with the Brown-Forsythe test, and normality with the Shapiro-Wilk test. Because several variables did not pass normality tests, pairwise correlations were analyzed using the conservative non-parametric Kendall's tau with correction for ties. Three-way ANOVA was performed using JASP version 10.11.1 (JASP Team 2019, Computer software). Two-and one-way ANOVAs, correlation and linear regression analysis were performed using Statview 5.0 software (SPSS, USA). The p values < 0.05 were considered statistically significant.

Network graphs were constructed to represent changes in SUV in each brain structure and correlations between SUVs of pairs of structures, depending on the genotype and experimental condition (STRESS and NO STRESS). In these graphs, each node represents one of the 12 brain regions studied. The size of the node represents the SUV in this region or the difference of SUV between genotypes in this region; values were normalized and a minimal size of node was assigned for graphical reasons, so the size of the node is (SUV of the region - smallest SUV) / (largest SUV - smallest SUV) x 40 + 10. A line linking two nodes represent a significant correlation between SUVs of the two brain structures. The width of the line represents Kendall's tau for graphs displaying data from one genotype; values were also normalized and a minimal width was assigned, so the width of the line is (Tau - smallest Tau) / (largest Tau - smallest Tau) x 5 + 2. For graphs comparing WT and *mdx* mice, the width of the line is proportional to the difference between the slopes of the linear regression lines of *mdx* and WT mice calculated for each pair of brain structures.

Networks graphs were built using igraph package in R 4.4.1, regression plots using Statview 5.0 and other graphs and correlation matrices using GraphPad Prism 9 (Dotmatics, USA).
RESULTS

The loss of dystrophin in the *mdx* mouse model has been associated with an abnormal unconditioned fear response to mild acute stress, characterized by long periods of tonic immobility (freezing) following a short manual scruff restraint. It has been demonstrated that unconditioned fear is strongly expressed in adult *mdx* males following scruff restraint but not in WT littermate male mice, while gentle tail handling does not elicit any fear response in both genotypes (Sekiguchi et al., 2009; Vaillend and Chaussenot, 2017). In two separate sessions undertaken with each mouse, the [18F]FDG PET data acquired in *mdx* and WT littermate male mice were compared following scruff restraint (STRESS) and following tail handling after a one-week recovery period (NO STRESS).

<figure><img src="image_2.png" /><figcaption>**Figure 1: Study design and results of behavioral test and PET-scanner.** (A) Study design. (B) Stress-induced unconditioned fear response. Immobility (% of WT) displayed by mice during 5 min following restraint and FDG injection; \*\*\*p<0.001. (C-D) Cerebral uptake of FDG expressed as Standardized Uptake Value (SUV) in 12 different brain regions in WT mice (C) and *mdx* mice (D), in the NO STRESS (white symbols) and STRESS (Black symbols) conditions. (E) Comparison of the effect of stress, expressed as the SUV in the STRESS condition minus the SUV in the NO STRESS condition, in WT and *mdx* mice. \# indicate structures for which ANOVAs detected significant genotype x condition interactions. Data expressed as mean ± SD. Cx: cortex; Thal: thalamus; Cb: cerebellum; BF: basal forebrain septum; Hyp: hypothalamus; BS: brain stem; Gray: central gray; OB: olfactory bulb; Str: striatum; Hipp: hippocampus; Amyg: amygdala; MidB: midbrain.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>

As shown in Fig. 1 B, freezing was strongly expressed by *mdx* mice compared to WT mice in the STRESS condition (3.5 fold higher) but not in the NO STRESS condition (n=7 per genotype and per condition) (genotype x stress interaction: F(1,12)=13.831, p<0.003). Post-hoc tests indicated that freezing was significantly higher in *mdx* mice compared to WT mice in the STRESS condition (p=0.0001), while the two genotypes displayed comparable freezing responses in the NO STRESS condition (p>0.9).

Brain uptake of [18F]FDG was assessed 50 min after behavioral testing, after restraint or gentle handling. One *mdx* mouse was excluded from both conditions, and one WT mouse was excluded from the NO STRESS condition for technical reasons. Comparisons were therefore performed using n=6 mice per genotype in the NO STRESS condition and n=6 *mdx* and n=7 WT in the STRESS condition. We first evaluated the impact of stress on [18F]FDG uptake, by analyzing the whole set of data using a three-way ANOVA with one grouping factor (genotype) and two within-factor repeated measures (emotional state: NO STRESS vs STRESS; brain structure: 12 different brain structures). This revealed a significant structure x genotype interaction (F(11,110)=2.82, p=0.003), a significant emotional state x genotype interaction (F(1,10)=5.46, p=0.04), and a significant structure x emotional state interaction (F(11,110)=2.81, p=0.003), suggesting that the emotional state had distinct effects in the two genotypes and differential impact among brain structures. This was further confirmed by analyzing the impact of the emotional state separately in each genotype (Fig. 1 C-D). Indeed, no significant impact of stress was detected in WT mice (stress effect: F(1,11)=0.03, p=0.95, NS; stress x structure interaction: F(11,121)=0.43, p=0.93, NS) (see Fig. 1 C). In contrast, in *mdx* mice (Fig. 1 D), stress significantly decreased [18F]FDG uptake, indicating decreased brain glucose metabolism (stress effect: F(1,10)=6.02, p=0.034).

Conversely, we also compared [18F]FDG uptake between genotypes in each experimental condition. In the NO STRESS condition (Supplementary Fig. S1 A), [18F]FDG uptake was comparable between genotypes (F(1,10)=1.13, p=0.31). The uptake levels differed among brain structures (F(11,110)=10.54, p<0.0001), but the genotype x structure interaction was not significant (F(11,110)=0.50, p=0.89), indicating that structure-dependent variations of [18F]FDG among brain structures were similar in the two genotypes. In the STRESS condition (Fig. S1 B), no main genotype effect was detected (F(1,11)=1.79, p=0.2), but the genotype x structure interaction was significant (F(11, 121)=5.78, p<0.0001). This suggested, again, that stress had a differential impact on the two genotypes depending on the brain structure.
To further analyze the differential effects of stress on genotypes in each brain structure, we then performed 2-way ANOVAs (genotype, emotional state) for each brain structure. The impact of stress significantly differed between genotypes in the basal forebrain, hypothalamus, brainstem, olfactory bulbs, and striatum (stress x genotype interactions: $F(1,10)<9.5, p<0.05$), but not in other brain regions. These differences in [18F]FDG uptake between the STRESS and NO-STRESS conditions are represented in Fig. 1 E, where the significant genotype x structure are indicated by the hash symbol on top of the plot. In the basal forebrain, hypothalamus, and brainstem, the significant interactions reflected changes in opposite directions depending on the genotype. Indeed, slightly increased activations were observed in WT mice in these structures (as can be seen in Fig. 1 C), while decreased activations were conversely observed in *mdx* mice. In the olfactory bulb and striatum, the significant interactions were associated with relatively larger quantitative differences between genotypes in the stress condition. This basically shows that in WT mice, the stress condition did not alter glucose uptake or induced slight increases (in basal forebrain, hypothalamus and brainstem), while in *mdx* mice stress induced a decreased uptake of variable amplitude depending on the brain structure.

**Figure 2: Correlations between Standardized Uptake Values (SUV) of FDG in 12 brain structures and the percentage of immobility during behavioral testing.** (A-B) Correlation matrix in NO STRESS condition (A) and STRESS condition (B) for WT and *mdx* mice. The strength of the correlations (-1 < r < 1) between FDG uptake in each brain structures and the percentage of immobility is represented by colors from green (negative correlation) to dark red (positive correlation) as indicated on the color-key scale (on the right of the matrix in A); Correlations close to zero are in yellow. Asterisks (*) indicate the significant correlations according to Kendall's statistics (p<0.05). (C-F) Examples of linear regression plots showing the relationships between immobility (%) and cerebral FDG uptake in the STRESS condition, respectively in cortex (C), olfactory bulb (D), hypothalamus (E) and basal forebrain (F) where significant correlations were detected in one genotype and not in the other one (WT: black symbols; *mdx*: red symbols). Brain: whole brain; Cx: cortex; Thal: thalamus; Cb: cerebellum; BF: basal forebrain septum; Hyp: hypothalamus; BS: brain stem; Gray: central gray; OB: olfactory bulb; Str: striatum; Hipp: hippocampus; Amyg: amygdala; MidB: mibrain.
<figure><img src="image_4.png" /><figcaption>Figure 2</figcaption></figure>

Another main outcome in our data analysis was the link between the fear phenotype and the activation of different brain structures. This was addressed by examining the correlations between the amount of freezing and brain [18F]FDG uptake in each structure, using the conservative non-parametric Kendall's tau with correction for ties for pairwise correlation analysis (Fig 2 and Supplementary Table S1). As shown in Fig. 2 A, all correlations were low (<0.5) and not significant in the NO STRESS condition for both WT and *mdx* mice. In contrast, the STRESS condition was associated with many positive and significant correlations between the percentage of immobility and [18F]FDG uptake. Still, two distinct profiles were observed between genotypes (Fig. 2 B): The correlations were significant in the whole brain, cortex, thalamus, olfactory bulbs, hippocampus, and midbrain in WT mice but not in *mdx* mice. On the contrary, the correlations were significant in the basal forebrain, hypothalamus, brainstem, and amygdala in *mdx* mice but not in WT mice. Fig. 2 C-F displays examples of linear regression plots for structures differentially correlated to freezing between WT and *mdx* mice.

To evaluate putative alterations in the connectivity between brain structures of *mdx* mice compared to WT mice, we also scrutinized the correlations between the activities of the different brain structures. Changes in SUV in each brain structure and correlations between SUVs of pairs of structures were represented using network graphs, showing changes depending on the genotype and experimental condition (STRESS and NO STRESS) (Fig 3). This revealed distinct profiles of pairwise correlations between WT and *mdx* mice, particularly in the STRESS condition:

**Figure 3: Correlations between Standardized Uptake Values of FDG in 12 brain structures.** (A-F) Network graphs representing Standard Uptake Values in 12 brain structures and correlations between these structures in
NO STRESS condition (A-C) and STRESS condition (D-F). A and D show results in WT mice, B and E in *mdx* mice, and C and F compare both genotypes. Purple nodes represent normalized SUV of the brain structure (see « Statistics and graphical representations » in the Materials and Methods section). Red and light blue nodes represent the normalized SUV difference (ΔSUV) between genotypes; red nodes indicate a higher SUV in *mdx* mice, light blue nodes indicate a lower SUV in *mdx* mice. In A-B and D-E, the lines represent a correlation between SUVs of two brain structures; grey lines when correlation is significant in both genotypes, blue lines when it is significant in WT mice only, and red lines when it is significant in *mdx* mice only. The thickness of the line represents normalized Kendall's tau (a thicker line representing a larger tau value). In C and F, green and orange lines represent the difference between the linear regression slopes of *mdx* and WT mice calculated for each pair of brain structures (as in the examples in G-K); orange lines indicate a larger slope for *mdx* mice and green lines a smaller slope for *mdx* mice. The thickness of the line is proportional to this difference. (G-K) Linear regression plots showing the relationships between FDG uptake levels measured in hypothalamus to those of cerebellum (G), central gray (H), hippocampus (I), striatum (J) or midbrain (K), in *mdx* (red symbols and lines) and WT mice (black symbols and lines) in the STRESS condition. Cx: cortex; Thal: thalamus; Cb: cerebellum; BF: basal forebrain septum; Hyp: hypothalamus; BS: brain stem; Gray: central gray; OB: olfactory bulb; Str: striatum; Hipp: hippocampus; Amyg: amygdala; MidB: mibrain.

<figure><img src="image_5.png" /><figcaption>Figure 3</figcaption></figure>

In the NO STRESS condition (Fig. 3 A-C), among the 66 pairs of structures, 32 were significantly correlated in both genotypes (grey lines), 5 were significantly correlated in WT mice only (blue lines, Fig. 3 A) and 4 in *mdx* mice only (Fig. 3 B; exact correlation and statistical values in Supplementary Table S2 A-B; correlation matrix in Fig S2 A and B). All significant correlations were positive. Additionally, the size of the nodes (purple circles) in Fig. 3 A-B reflect the normalized SUVs of each structure, confirming that structure-dependent
variations of SUV among brain structures were similar in the two genotypes. When there was a significant correlation between the SUVs of two structures in at least one genotype, we also constructed the corresponding linear regression plot. We then calculated the difference of the linear regression slopes between the two genotypes (*mdx* minus WT) to further highlight potentially relevant differences. These differences are represented in Fig 3 C, orange lines indicating a larger slope for *mdx* mice and green lines a smaller slope for *mdx* mice, with the thickness being proportional to this difference. In the NO STRESS condition, all absolute differences between slopes were below 0.5 (represented by thin lines in Fig. 3 C, and in matrix of Supplementary Fig S2 C). The red nodes in Fig 3 C indicate a slightly higher SUV in *mdx* mice in the NO STRESS condition, which was already observable in Supplementary Fig. S1.

In the STRESS condition, 19 pairs of structures were significantly correlated in both genotypes (grey lines), 17 significantly correlated in WT mice only (blue lines) and 10 in *mdx* mice only (Fig. 3 D-E; exact correlation and statistical values in Supplementary Table S3 A-B; correlation matrix in Fig S2 D and E). The size of the purple nodes in Fig. 3 D-E confirms a general decrease of SUV values in *mdx* mice in this condition. The variations in SUV and the linear regression slope differential between the two genotypes are shown in Fig. 3 F. The size of the light blue nodes confirm a lower SUV in *mdx* mice in most structures, except in the amygdala (red node) where SUV was conversely slightly higher in *mdx* mice (also observable in Supplementary Fig. S1 B). Moreover, in this STRESS condition, much larger differences were detected regarding the linear regression slope differential (Fig. 3 F; matrix in Supplementary Fig. S2 F). The largest differences were observed for the correlation of the hypothalamus with the cerebellum, central gray, hippocampus, striatum, and midbrain (Thick green lines connected to the hypothalamus in Fig. 3 F and plots in Fig. 3 G-K). The differences in these structures were respectively of 1.63, 1.41, 1.28, 1.15 and 0.95, with lower regression slopes for the *mdx* mice. All other differences were below 0.7.

To summarize this last analysis, in the NO STRESS condition, even if few correlations were statistically different between genotypes, the linear regression slope differentials were not drastically different between genotypes, suggesting globally similar connectivity among brain structures. However, in the STRESS condition, the linear regression-slope differentials between genotypes were larger and concerned the relationships between hypothalamic metabolic activity and the metabolic activity of the cerebellum, central gray, hippocampus, striatum, and midbrain.

DISCUSSION

Previous studies have reported that Dp427-deficient *mdx* mice have an abnormal fear response, which has been associated with alterations in the responsiveness of the amygdala to adrenergic inputs and GABAergic transmission (Sekiguchi et al., 2009). As the amygdala is a key component of the fear circuit and has many connections throughout the brain, these alterations might affect the activation of other brain structures. Moreover, disrupted GABAergic inhibition has been characterized in other brain structures lacking Dp427 in *mdx* mice, and this has been associated with changes in synapse structure and plasticity. However, the dysfunctional circuit involved in this phenotype has not been investigated to date. The question remains as to whether the structures connected to the amygdala could be under or over-activated. In this study, the spatial resolution of our PET scanner enabled quantitative
imaging of the whole brain and discrimination of 12 different brain structures, in basal state or
after stress, in *mdx* and WT littermate mice. We demonstrate that the activation of these
cerebral regions is equivalent between genotypes in the unstressed condition, while a global
decrease in brain glucose metabolism, suggesting decreased neuronal function, is observed in
*mdx* mice compared to WT mice after the stress-induced fear response. The connectivity
between these cerebral regions and the correlation of their activation with behavioral
parameters is also similar in the unstressed condition in both genotypes but different
following a mild acute stress-inducing fear response in *mdx* mice.

In contrast to WT mice, a significant effect of stress was found in *mdx* mice, which showed a lower brain glucose metabolism in the stress condition. In light of the current literature, both central and peripheral modifications might contribute to interpreting this phenotype (Gharibi et al., 2024). Possible peripheral factors include a change in blood pressure since systemic hypotension is reported in DMD patients, likely due to the loss of Dp427 in vascular smooth muscles (Shen et al., 2023). In mice, it has been shown that WT males display a hypertensive response following restraint stress, while *mdx* males have a hypotensive response (Lindsay and Russell, 2023). Moreover, an age-dependent reduction of cerebral blood flow has been detected by MRI in *mdx* mice (Goodnough et al., 2014). Decreased cerebral blood flow is a potential factor that might influence a reduction of cerebral [18F]FDG uptake. Stress-induced increase in peripheral blood glucose might also induce a competition between glucose and [18F]FDG at the glucose transporter level. However, mice in the present study were younger than those in which a previous study reported decreased cerebral blood flow. They were food-deprived, and the decreased [18F]FDG uptake was not observed in wild-type mice. Moreover, the impact or STRESS on [18F]FDG uptake was not global but significantly different across brain regions. Hence, our results suggest that the stress-induced change in [18F]FDG uptake observed in *mdx* mice cannot be simply attributed to a change in peripheral blood glucose or global change in cerebral blood flow.

We report here that the impact of STRESS on brain glucose metabolism differed between genotypes, particularly in the basal forebrain, hypothalamus, brainstem, olfactory bulbs, and striatum. These selective stress effects suggest that region-specific brain dysfunctions are involved. The brainstem is key in autonomic regulation, including blood pressure control. Therefore, its hypoactivation might be linked to the hypotensive response displayed by *mdx* mice during their stress-induced fear response. It may also be hypothesized that hypoactivation of the basal forebrain after stress might reflect an abnormal integration of the stressful stimulus in *mdx* mice, as this structure is involved in encoding aversive or positive stimuli (Ananth et al., 2023). Two of the other most affected structures, olfactory bulbs, and striatum, are normally activated during exploration and goal-directed locomotion, and their hypoactivation after stress might reflect the reduced motor activity associated with the fear-induced immobility (freezing response) of *mdx* mice. The hypoactivation of the striatum and hypothalamus (further discussed below) may seem unexpected since these structures do not show overt expression of the Dp427 protein (Knuesel et al., 1999), suggesting that their differential activation could be due to a modification of inputs from other regions and not to any change in the physiology of the structure itself.

Differences between WT and *mdx* mice were also detected concerning the correlation
between the activation of the different brain structures and the percentage of time spent
immobile (freezing response reflecting unconditioned fear) after exposition to the stressful
stimulus. In WT mice, the structures whose activities were correlated to the time spent
immobile are known to be involved in motor control and exploration. The cortex, thalamus,
and midbrain play an important role in locomotor activity and motor planning), which are likely regulated during periods of immobility (Boecker et al., 2008; Gao et al., 2018). Moreover, immobility does not preclude olfactory exploration, which may explain the correlation with the activity of the olfactory bulbs. Distinct hippocampal networks play important roles in coding the animal's current position during locomotion and immobility (Arriaga and Han, 2017; Kay et al., 2016), and hippocampal place cells fire in sequences that replay the animal's previous paths (Foster and Wilson, 2006) or predict future paths (Pfeiffer and Foster, 2013).

Whereas in WT mice, the short episodes of immobility typically correspond to transient locomotor arrest during exploration, in *mdx* mice, the time spent immobile was 3-4 fold longer and is believed to reflect a defensive emotional state (freezing) (Sekiguchi et al., 2009). Interestingly, we found that stress-induced immobility in *mdx* mice correlates with the activity of structures involved in freezing, a typical behavior expressed by mice during unconditioned fear. Indeed, the basal forebrain, hypothalamus, brainstem, and amygdala are all part of the freezing network (Pernía-Andrade et al., 2021; Roelofs and Dayan, 2022). The basal forebrain sends cholinergic projections to olfactory bulbs to enhance the detection of odors (Rothermel et al., 2014), as well as to the amygdala (Carlsen et al., 1985). A reduction of time freezing is observed when this latter projection is disrupted (Power and McGaugh, 2002). The basal forebrain (via the midbrain) and amygdala also influence the activation of the vagal nuclei of the brainstem, which is the main effector of the parasympathetic response, leading notably to low blood pressure. The hypothalamus is also part of a central pathway controlling sympathetic activity and the interplay between anxiety, emotional stress, and blood pressure (Fontes et al., 2023). Interestingly, the hypothalamus was the only structure showing a volume reduction in a post-mortem anatomic MRI study in *mdx* mice (Kogelman et al., 2018). This further supports a main alteration of hypothalamus structure and function.

Although immunohistochemical studies have shown that Dp427 expression is enriched in the cerebellum, hippocampus, amygdala, and cerebral cortex, a more discrete expression in other brain structures cannot be excluded. When Dp427 is absent, altered synaptic development, function, and plasticity in regions that normally express Dp427 may also compromise connectivity with other structures that do not express dystrophin. Our analysis of glucose uptake correlations between structures suggests the activation of distinct central pathways in *mdx* and WT mice following stress. In the unstressed condition, even if 8 pairs of structures were statistically correlated in one genotype but not the other one, none of the differences appeared to be physiologically relevant, based on our quantification of the linear relationships between structures' activations. After being exposed to stress, more pairs of structures (27) were differentially correlated between WT and *mdx* mice. Moreover, the differences appeared particularly important for the correlation of the hypothalamus with the cerebellum, central gray, hippocampus, striatum, and midbrain. Thus, although the connectivity between brain structures seems relatively similar between genotypes in basal (unstressed) conditions, the neuronal networks involved in the response to stress appear to differ between WT and *mdx* mice, notably the neuronal networks involving the hypothalamus. One of the main functions of the hypothalamus is its neuroendocrine function. The hypothalamic-pituitary-adrenal axis regulates the release of glucocorticoids and vasopressin in response to stress, two hormones implicated in the control of blood pressure (Smith and Vale, 2006). Moreover, this hypothalamic response to stress is modulated by inputs from structures correlated to freezing in *mdx* mice, such as the brainstem and the amygdala (Krukoff et al., 1995; Smith and Vale, 2006). Seminal studies showed that basal levels of serum corticosterone are comparable between *mdx* and WT mice (Yamamoto et al., 2010) despite a decreased basal serum level of
adrenocorticotropic hormone (ACTH) (Vaillend and Chaussenot, 2017). Additionally, both studies showed that a single restraint-induced fear response initially triggers comparable increases in the levels of these two hormones in *mdx* and WT mice, which suggested a slight or compensated dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis in Dp427-deficient mice. More recently, it was shown that scruff restraint or single episodes of social stress may induce larger increases in the level of circulating corticosterone in *mdx* mice compared to WT mice at longer delays (30 min after scruff restraint and 6h after social stress), with levels returning to baseline after 24h (after 6h following scruff restraint). However, inhibition or exogenous injection of corticosterone did not affect this phenotype, which, therefore, does not appear to be solely attributable to a dysfunctional HPA axis (Gharibi et al., 2024; Lindsay et al., 2021). Besides the hypothalamic-pituitary-adrenal axis, the hypothalamic-pituitary-gonadal axis controls estrogen levels. Recent data suggest that estrogens could have an effect on adrenal function and could thus partially restore a normotensive response to stress in *mdx* mice (Lindsay and Russell, 2023). The putative hypothalamic processes affected by Dp427 loss and contributing to the abnormal stress reactivity of *mdx* mice are, therefore, still unclear. Our present data suggest altered connectivity between the hypothalamus and other brain structures involved in the fear circuit. Future studies should further investigate the complex interplay between the dysregulated control of blood pressure, estrogen, and stress hormone levels in DMD.

The present results suggest that, in response to stress, dystrophin-deficient *mdx* mice use different cerebral networks compared to WT mice, with the involvement of the fear circuit and another pathway involving the hypothalamus. PET imaging in mice has a limited spatial resolution and did not allow us to discriminate the activation of small nuclei and sub-regions within the 12 brain structures studied here. Future studies in larger animals, such as pig or dog DMD models (Zaynitdinova et al., 2021), or brain imaging of DMD patients in response to a fearful stimulus (Maresh et al., 2022) might help to refine this altered neuronal network. Functional MRI cannot study the activation of cerebral regions in response to stress in anesthetized mice because it would not reflect the stress experienced by animals, contrary to our present PET scanner study in which [18F]FDG uptake occurs during expression of the fear response. However, diffusion MRI and tractography could be used to further document the differential connectivity between structures.

Future studies shall also elucidate the neural bases of the complex genotype-dependent expression of brain-related phenotypes. Indeed, DMD patients lacking several dystrophins have more severe cognitive and behavioral disturbances (Ricotti et al., 2016) and may show more serious abnormalities in gray matter volume (Doorenweerd et al., 2014), cerebral blood flow (Doorenweerd et al., 2017a), and brain tissue diffusivity (Preethish-Kumar et al., 2020). This could be addressed in comparative neuroimaging analyses of mouse models lacking distinct brain dystrophins, such as *mdx*52 mice (lacking Dp427, Dp260, and Dp140), or in a mouse model lacking all dystrophins (Araki et al., 1997; Im et al., 1996; Kudoh et al., 2005; Wertz and Füchtbauer, 1998). When relationships between brain functioning, genotype, and behavior are better characterized, neuroimaging could constitute an additional and relevant tool to identify DMD children at high risk of early behavioral problems. It would be useful to adapt medical care to their specific genotype-dependent brain-related comorbidities. It is equally important to better understand the consequences of brain Dp427 deficiency because it concerns all DMD patients regardless of their genotype and because some emotional and neuropsychiatric disorders appear to increase with age in DMD patients
(Gharibi et al., 2024). Importantly, it was shown in *mdx* mice that chronic stress may indirectly worsen muscle degeneration (Lindsay et al., 2021), suggesting that a better management of stress in DMD patients might slow down the progression of muscular dystrophy. Moreover, DMD patients are primarily treated with corticosteroids to improve muscle function. Still, there is currently no information regarding the putative impact of this treatment on other physiological functions, such as blood pressure and stress reactivity.

The present study identifies altered brain glucose metabolism as neural substrate of increased reactivity to stress in DMD, allowing us to better understand this multifactorial and severe phenotype. A variety of pharmaceutical interventions could be considered to better manage stress in DMD. Many could be easily tested in the *mdx* mouse model, for which several relevant behavioral, physiological, and neuroimaging outcome measures are now identified.

**Author contributions:**

Conceptualization and Methodology, C.V., F.R. and N.T.; Project design, funding acquisition and resources, C.V. and F.R.; Design of experiments, C.V., S.G. and N.T.; Experimental work: S.G. and C.V.; Data analysis, S.G. and M.L.; Original draft preparation, M.L. and C.V. All authors have contributed to proofreading the manuscript.

**Funding:**

This work was supported by the Centre National de la Recherche Scientifique (CNRS, France), Université Paris-Saclay (France), Commissariat à l'Énergie Atomique et aux énergies alternatives (CEA, France) and a strategic project grant to C.V. from A.F.M. (Association Française contre les Myopathies, France). M.L. was recipient of a research fellowship for medical resident from A.R.S. (Agence Régionale de Santé Occitanie, France). Experiments were performed on a platform of France Life Imaging network (grant ANR-11-INBS-0006).

Cyrille Vaillend reports financial support was provided by AFM-Téléthon. Marion Lancien reports financial support was provided by Regional Health Agency Occitanie. Nicolas Tournier reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Institutional Review Board Statement:**

All experiments have authorizations from the French Research Ministry and followed the guidelines of our mouse facility (agreement #D91-471-104) in compliance with European Directive 2010/63/EU, French National Committee (87/848) and local Ethics Committee (Paris Centre et Sud, #59).

**Declaration of interests**

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
**Informed Consent Statement:**

Not applicable.

**Data Availability Statement:**

The primary data for this study are available from the authors upon request.

**Acknowledgments:**

We are grateful to the Zootechnic platforms of the Paris-Saclay Institute of Neuroscience and Service Hospitalier Frédéric Joliot for mouse breeding, care, and the genotyping platform at Paris-Saclay Institute of Neuroscience.

**Conflicts of interest:**

The authors declare no conflict of interest.

## HIGHLIGHTS

☐ Duchenne muscular dystrophy (DMD) is associated with neurobehavioral deficits

☐ Enhanced stress reactivity is a major phenotype in the mdx mouse model of DMD

☐ We used in vivo [18F]FDG PET imaging to study brain glucose metabolism in mdx mice

☐ We show that stress reduces glucose uptake in the brain fear circuit in mdx mice

☐ We also identified altered connectivity of hypothalamus to other brain structures

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