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# Western diet since adolescence impairs brain functional hyperemia at adulthood in mice: rescue by a balanced ω-3:ω-6 polyunsaturated fatty acids ratio

Haleh Soleimanzad, Clémentine Morisset, Mireia Montaner, Frédéric Pain, Christophe Magnan, Mickaël Tanter, Hirac Gurden

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Haleh Soleimanzad, Clémentine Morisset, Mireia Montaner, Frédéric Pain, Christophe Magnan, et al. Western diet since adolescence impairs brain functional hyperemia at adulthood in mice: rescue by a balanced ω-3:ω-6 polyunsaturated fatty acids ratio. International Journal of Obesity, 2025, 49 (5), pp.844-854. 10.1038/s41366-025-01711-x. hal-05064176

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https://hal.science/hal-05064176v1

Submitted on 7 Dec 2025

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1 **Western diet since adolescence impairs brain functional hyperemia at adulthood in mice:**

2 **rescue by a balanced ω-3:ω-6 polyunsaturated fatty acids ratio**

3 Haleh Soleimanzad.¹§, Clémentine Morisset¹§§, Mireia Montaner²#, Frédéric Pain³, Christophe

4 Magnan², Mickaël Tanter¹*, Hirac Gurden²**

5 ¹Physics for Medicine Paris, ESPCI Paris, INSERM, CNRS, PSL Research University, 75015 Paris, France.

6 ²Université Paris Cité, Unit of Functional and Adaptive Biology (BFA), UMR 8251 CNRS, 75013 Paris,

7 France.

8 ³Université Paris-Saclay, Institut d'Optique Graduate School, CNRS, Laboratoire Charles Fabry, 91127,

9 Palaiseau, France.

10 ⁵HS and ⁵⁶CM have contributed equally to this work.

11 #MM present affiliation: Institute of Metabolic Science & MRC Metabolic Diseases Unit, University
12 of Cambridge, Cambridge, UK.

13 *MT and **HG are co-corresponding authors.

14

15
16 **Abstract**

17 **Background/Objective:** Obesity is a devastating worldwide metabolic disease, with the highest
18 prevalence in children and adolescents. Obesity impacts neuronal function but the fate of functional
19 hyperemia, a vital mechanism making possible cerebral blood supply to active brain areas, is unknown
20 in organisms fed a high-caloric Western Diet (WD) since adolescence.

21 **Subjects/Methods:** We mapped changes in cerebral blood volume (CBV) in the somatosensory
22 cortex in response to whisker stimulation in adolescent, adult, and middle-aged mice fed a WD since
23 adolescence. To this aim, we used non-invasive and high-resolution functional ultrasound imaging
24 (fUS).

25 **Results:** We efficiently mimicked the metabolic syndrome of adolescents in young mice with early
26 weight gain, dysfunctional glucose homeostasis, and insulinemia. Functional hyperemia is
27 compromised as early as 3 weeks of WD and remains impaired after that in adolescent mice. Starting
28 WD in adult mice does not trigger the profound impairment in sensory-evoked CBV observed in young
29 mice, suggesting a cerebrovascular vulnerability to WD during adolescence. In WD, ω-6:ω-3
30 polyunsaturated fatty acids (PUFAs) ratio is unbalanced towards proinflammatory ω-6. A balanced ω-
31 6:ω-3 PUFAs ratio in WD achieved by docosahexaenoic acid supplementation efficiently restores
32 glucose homeostasis and functional hyperemia in adults.

33 **Conclusions:** WD triggers a rapid impairment in cerebrovascular activity in adolescence, which is
34 maintained at older ages, and can be rescued by a PUFA-based nutraceutical approach.

36 **Keywords**

37 Obesity. Cerebral blood flow and volume. Functional ultrasound imaging. Docosahexaenoic acid.

38
39

**Introduction**

40

According to the World Health Organization, obesity is particularly affecting the youngest populations.

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Very low in the 1970s, the prevalence of overweight and obesity among the 5-19-year-olds reached 18% in 2016 [1]. This range is by far the one with the highest increase in indicators of obesity [2].

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Obesity is a global health threat resulting from excessive consumption of energy-dense food and insufficient calorie expenditure [3]. In consequence, obesity is primarily defined by the excessive fat accumulation in the body [4]. This disease predisposes patients to numerous pathologies such as type

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2 diabetes (T2D), nonalcoholic fatty liver disease, and cardiovascular diseases [3] by hampering the function of organs such as the pancreas, the liver, and the heart [5]. Obesity also impacts several aspects of brain function, including sensory and cognitive processing, in rodent models [6,7] and humans [8,9]. In patients with obesity, a very high intake of energy is due to the frequent ingestion of

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Western Diet (WD) enriched in both saturated fatty acids and refined carbohydrates. WD is notably characterized by a deleterious unbalanced ω-3:ω-6 polyunsaturated fatty acids (PUFAs) ratio and causes gluco- and lipo-toxicity in cerebral tissues [10,11]. This impacts fine neuronal and cerebrovascular function [7, 12].

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The very high energy demand of neuronal activity is fulfilled by a vascular supply which has to be instantly regulated [13]. In brain networks, coordinated neurovascular coupling mechanisms make possible functional hyperemia, i.e. the increase in cerebral blood flow (CBF) and volume (CBV) in response to increased synaptic activity [14]. Failure in proper regulation of functional hyperemia is observed in diseases such as chronic cerebral hypoperfusion [15] leading to vascular dementia and pathogenesis underlying Alzheimer's disease [16]. Obesity is also associated with reduced functional hyperemia in adults and elderly [12] and, together with T2D [17], is a metabolic condition where the brain is susceptible to pathologies like ischemic stroke and vascular dementia [16]. Much effort has been made to characterize brain cerebrovascular activity in metabolic diseases in adult and old ages [18] and the context of ischemia (in rodents [19] and humans [7]) but changes in functional hyperemia

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64 from adolescence to mid-age in lean organisms versus organisms with obesity have not been
65 examined.

66 To follow up on functional hyperemia, several complementary imaging techniques were used in
67 animals [20] and humans [21]. Thanks to the typical 50-fold increase in terms of sensitivity to CBF
68 obtained using ultrafast Doppler imaging [22,23], a new concept of ultrasound neuroimaging emerged
69 during the last decade [24]. Such functional Ultrasound (fUS) imaging brought significant insights into
70 the understanding of brain function with neurodevelopmental, sensory, motor, and cognitive studies
71 [24]. Recent fUS imaging also demonstrated its ability to assess brain dysfunction in diseased models
72 [25] but metabolic pathologies were not examined using this efficient technique for intact in vivo
73 imaging recordings so far.

74 We decided to follow up on changes in energy metabolism and sensory-evoked functional hyperemia
75 in adolescent animals chronically fed with WD. Our data demonstrate that WD resulted in obese and
76 prediabetic mice characterized by hyperglycemia, glucose intolerance, insulin resistance, and
77 hyperinsulinemia compared to lean mice. These obese mice presented an early and sustained
78 impairment in functional hyperemia from adolescence to mid-age. We further showed that using
79 docosahexaenoic acid-supplemented WD to balance the ω-3:ω-6 PUFAs ratio during adolescence
80 prevents the deleterious effects on metabolic and cerebrovascular functions linked to WD in adult
81 obese mice.

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## 83 RESULTS

Starting WD at adolescence induces profound metabolic deficiency in adolescent, adult, and middle-aged mice.

First, we compared the main metabolic functions of WD-fed mice (5317 kcal/kg), starting at adolescence and maintained under WD until middle age, to age-matched regular diet (RD)-fed control mice (2791 kcal/kg). We characterized the metabolic phenotype of 1 week (W), 2W, 3W, 2 months (M), and 10M WD-fed and RD-fed groups (Fig. 1a). Although no significant differences in body weight nor body composition were shown after 1 or 2 weeks of WD (1W and 2W groups, Fig. 1b,c), 3 weeks of WD caused significant body weight (3W group, Fig. 1b) and fat mass (Fig. 1c) gain in WD-fed mice. Mice with 3 weeks of WD displayed a significant rise in fasting blood glucose (Fig. 1d) compared to RD mice, a major metabolic defect not present at 1 or 2W of WD. We observed that this deleterious effect of WD on glycemia persisted until adulthood (2M group) and middle age (10M group). In addition, the Oral Glucose Tolerance Test (OGTT) showed increased glucose intolerance (Fig. 1e, also see time course of glycemia in Supplementary Figure 1a-b) and the Insulin Tolerance Test (ITT) showed decreased insulin sensitivity (Fig. 1f). Thus, at 3 weeks of WD, significant weight and fat mass gain was accompanied by insulin resistance and impairment in glucose metabolism. Altogether these parameters define the state of obesity in our DIO model starting after 3 weeks of WD. All these metabolic parameters worsened from 3 weeks to 10 months of WD. We also observed increased basal insulinemia in all WD-fed groups (Fig 1g-k), with a loss of glucose-stimulated insulin secretion. Thus, feeding mice with WD during adolescence led to obese and prediabetic states in adolescence, adulthood, and mid-age. WD-induced obesity in mice is an accurate model to mimic the metabolic syndrome in humans [26].

fUS imaging reveals rapid and long-lasting impairment of functional hyperemia in mice fed a WD since adolescence.
We imaged sensory-evoked hemodynamic responses non-invasively in the barrel cortex of anesthetized WD-fed and age-matched RD-fed mice in response to mechanical whisker stimulation at 2Hz. We observed transcranial cerebrovascular responses using fUS imaging with a 15MHz ultrasonic array placed over a coronal plane covering the somatosensory cortex (Fig. 2a). To detect activated brain areas during whisker stimulation, we computed Z-scores based on general linear model analyses (GLM) with Bonferroni correction for multiple comparisons (normalization by the total number of pixels, see Methods). Computing the fUS activation map clearly showed an area of activation in the contralateral barrel cortex in response to whisker stimulation in all mice groups (Fig. 2b and 2d). To further characterize sensory evoked-CBV, we quantified the mean z-score (Fig. 2e, see material and methods) to detect the extent of activated pixels and the mean variation of relative CBV (mean ΔrCBV, % of baseline, Fig 2c left and 2g) over the responding activated pixels (ΔrCBV map, Fig. 2f). We observed that highly activated pixels were further located inside the left barrel cortex [24] in response to right whiskers mechanical stimulation. Then, we computed the averaged rCBV time course through stimulation trials (Fig. 2c right and 2h) and observed that it systematically showed a transient and stimulus-locked increase in activated areas in all groups. We also calculated the mean ΔrCBV amplitude (at the plateau between 10 to 30 seconds of whisker stimulation) over baseline (mean %ΔrCBV, Fig. 2g). No significant differences were observed by paired comparison of WD and RD groups with 1 week, 2 weeks and 10 months of diet but we could reveal a significant reduction in z-score and ΔrCBV in groups with 3 weeks and 2 months of WD. Although ΔrCBV is not different between 10M RD and WD groups, z-score is significantly diminished in the 10M WD compared to RD controls (Fig 2e-f). Thus, feeding mice with WD resulted in a strong and long-term impairment of functional hyperemia in the somatosensory cortex of mice.

**Sensory-evoked functional hyperemia dynamics during development and aging are different in lean versus obese mice.**
We further analyzed fUS signals and computed values of rise slope and maximum ΔrCBV variation from the signal time course at different ages (Fig 3a-b). RD mice showed a significant decrease in the slope value in the 3W compared to the 1W group (Fig. 3a). This decrease was still present at 2 months and 10 months of diet. Interestingly, this result fits with the physiological decline of cerebral hemodynamics during adolescent brain development in humans [27]. The same pattern of slope decrease was present in WD-fed mice as they age but the decline was further amplified during obesity (Fig 3a). Interestingly, ΔrCBV at middle age in the 10 months RD-fed mice was significantly decreased compared to young 1-2-week control groups (Fig. 3b). This could also explain the similar decreased hemodynamic response magnitude in WD and RD observed in the 10M group (compare Fig. 2g and Fig. 3b). In fact, aging dynamics of ΔrCBV is very different between obese and control mice (Fig. 3b-c): a sudden decrease of the ΔrCBV amplitude occurred in 3W WD-fed mice and remained impaired in 2M, whereas in RD-fed animals the decrease was present at middle age only (10M) [6]. This observation is in favor of the existence of premature aging mechanisms that could occur in WD-fed mice [6,28].

## Beginning WD in adolescence or adulthood negatively impacts functional hyperemia

Since the deleterious effect of WD on the hippocampal function in juvenile rodents was shown to be stronger than in adults [29], we wondered whether the age of WD onset could be an important factor for the negative impact of the Western diet on CBV. To answer this question, adolescent versus adult mice were WD-fed for 3 weeks, resulting respectively in 2 month-aged animals (2M, early WD onset, Fig. 3d-left panels) and 9 month-aged mice (late WD onset, Fig. 3d-right panels). Starting WD in adulthood resulted in a slightly impaired slope (p<0.05) and ΔrCBV (p<0.05) values 3 weeks after WD onset. The same protocol applied to adolescent mice resulted in no change in slope but strongly compromised the amplitude of the cerebrovascular activity (p<0.001 for ΔrCBV comparison). This result indicates that neuroplastic compensatory mechanisms do not prevent cerebrovascular
impairment during WD in the younger mice [30] and that cerebral blood regulation is sensitive to WD even in adulthood [7].

**Using docosahexaenoic acid (DHA) supplementation to balance the ω-3:ω-6 PUFAs ratio to 1 restores glucose tolerance and insulin sensitivity in WD-fed mice.**

WD in humans is characterized by an unbalanced ω-3: ω-6 PUFAs ratio (from 1/10 to 1/20), in favor of ω-6 PUFAs [31]. The WD used in our experiments has a ratio of 1/11, in the range of WD consumed by humans. Since DHA, a ω-3 PUFA highly concentrated in the brain, has promising protective effects on the cardiovascular system [32], including on the cerebral blood vessels in ischemic models [33], we decided to add DHA to WD to restore an accurate ω-3:ω-6 PUFAs ratio of 1. To evaluate how this balanced ratio of fatty acids affects metabolism, adolescent mice were fed with either RD, WD, or WD enriched with DHA (WD+DHA) for 2 months before reaching adulthood. Body weight (Fig. 4a) and fat mass (Fig. 4b) were significantly increased in both WD-fed and WD+DHA-fed groups compared to RD.

Interestingly, fasting glycemia in WD+DHA mice had an intermediate profile, significantly reduced compared to WD but higher compared to RD (Fig 4c). In addition, supplementing mice with DHA resulted in selective recovery of glucose tolerance (AUC of the OGTT, Fig. d, also see time course of glycemia in Supplementary Figure 1c) and insulin sensitivity (AUC of the ITT, Fig. e) in the WD+DHA compared to the WD group (Fig. 4d-e). DHA supplementation also resulted in decreased baseline levels of insulinemia (t0) and decreased glucose-induced insulinemia (t15, Fig. 4f) in the WD+DHA group compared to WD mice, further showing the gain of efficacy in insulin action under balanced ω-3:ω-6 PUFAs ratio [34]. We conclude that restoring a balanced ω-3:ω-6 PUFAs by DHA supplementation restores major metabolic parameters in obese mice.

**A ω-3:ω-6 PUFAs ratio of 1 prevents functional hyperemia deficits in WD-fed mice.**

To test whether the metabolic improvement triggered by balanced ω-3:ω-6 PUFAs PUFAs ratio could positively influence functional hyperemia, we recorded whisker-evoked CBV increase by fUS imaging. As observed before, the mean activation map (Fig. 5a) and the mean ΔrCBV map (Fig. 5b) showed an
intense activation across the barrel cortex in RD-fed mice whereas the activated area was restricted in WD-fed mice. Interestingly, RD and WD+DHA mice showed a similar signal increase (Fig. 5a-b). We also observed a similar temporal evolution of the CBV signal in WD+DHA and RD-fed mice (Fig. 5c). These results indicate that DHA supplementation cancels the decrease in fUS signal observed with WD alone.

Computing the average z-scores of all significant pixels (Fig. 5d) from the ΔrCBV map (Fig. 5b) revealed a decreased activated area in WD compared to RD (Fig. 5d, top), an equal mean between WD and WD+DHA mice (Fig. 5d, middle) and a significantly higher value for WD+DHA group compared to WD (Fig 5d, bottom panel). This shows again the protective role of DHA on functional hyperemia in obese mice. In addition, the WD-fed mice showed a significant decrease in ΔrCBV compared to RD controls (Fig. 5e, top panel) while WD+DHA and RD mice were not different (Fig. 5e, middle panel). WD+DHA mice presented a trend toward ΔrCBV increase compared to WD-fed mice, but this difference was not significant (Fig. 5e, bottom). Thus, a balanced ω-3:ω-6 PUFAs ratio resulted in the improvement of sensory-evoked cerebrovascular responses compared to an unbalanced ratio. This result complements and strengthens, in the context of adolescent obesity, previous observations that DHA is beneficial to brain vascular function in obesity [35].

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Discussion

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In this study, feeding WD mice at adolescence resulted in glucose-intolerant, insulin-resistant, and hyperinsulinemic obese prediabetic mice. This metabolic disorder starts three weeks after WD onset at adolescence and is amplified in adulthood and middle age. fUS imaging recordings demonstrate an early deficiency in functional hyperemia in mice after the beginning of WD at adolescence. fUS further revealed decreased functional hyperemia in middle age in lean mice and a long-lasting cerebrovascular impairment in adulthood and middle age in obese mice. A balanced ω-3:ω-6 PUFAs ratio prevents functional hyperemia deficits induced by WD in adulthood.

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Childhood and adolescent obesity is a worldwide devastating disease and has become a major global public health concern [36]. It is a major driver of type 2 diabetes risk [37,38] at early ages. In line with previous studies [39, 40], our diet-induced obesity model revealed disrupted metabolic regulation with early impaired insulin sensitivity and decreased capacity in glucose regulation at the beginning of WD. Our murine model is also characterized by an increased fasted glycemia, which defines the appearance of a prediabetic phenotype in mice and fits with the adolescent metabolic syndrome. At this early age, failure rates in achieving optimal glycemic control are high, and young diabetic patients rapidly develop severe complications, including multiple defects in brain function [9]. The cerebrovascular activity is highly sensitive to pathological metabolic changes in the organism. Hampered functional hyperemia in our obese prediabetic mice is detected as soon as 3 weeks after WD onset and these mice never recover. This fits the human cerebrovascular picture. Increased BMI in adults is detrimental to CBF regulation [12, 41]. Young adult patients with obesity in their early twenties present lower CBF than healthy young subjects [42]. Importantly, obesity-induced insulin resistance in children and adolescents is the most important risk factor for vascular diseases in adulthood [43]. Early-onset T2D is a more aggressive disease from a vascular standpoint than adult T2D. In that sense, diabetes in adolescent patients with obesity further decreases CBF compared to adolescent patients with obesity alone [44].

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Indeed, our data show that the decline in function hyperemia is not only present in adolescence but is amplified and sustained in older, adult and middle-aged mice fed a WD since adolescence. Previous publications described an early impairment in functional hyperemia with WD starting in adulthood [45, 46]. Here, we further revealed that the same duration of WD is also harmful to metabolic and cerebrovascular regulation in adolescent mice. This pattern highlights the vulnerability of the adolescent brain to WD toxicity. Our results are in line with the cognitive impairment triggered by exposure to obesogenic diet in adolescent mice [29, 47]. They complete studies reporting global vulnerability of the neuronal and vascular network of the adolescent brain facing chronic neurotoxicity (substance abuse, mild traumatic brain injury, stress, WD...) [30, 48, 49].

Very interestingly, we also revealed two periods of normative functional hyperemia decrease in lean, metabolically healthy mice. First, decreased cerebrovascular activity in lean adolescent mice fits with the physiological decrease of cerebral hemodynamics during brain development in healthy human adolescents [27, 50, 51]. Secondly, impaired functional hyperemia in middle-aged mice is likely to correspond to a normative decrease in cerebrovascular activity in middle-aged humans [52]. More interestingly, our results about the blunted functional hyperemia in young adolescent obese mice, together with publications showing faster decreased CBF in the olfactory bulb of obese mice [6] and the accelerated vascular aging among adolescents with obesity and/or T2D [27, 53], suggest a premature aging of the cerebrovascular function in young organisms with obesity.

Anesthesia could be an issue whenever studying cerebrovascular dynamics. Isoflurane anesthesia has been shown to impact functional connectivity negatively [54]. Therefore, we used ketamine/xylazine anesthesia, a mixture injected i.p. and commonly used for fUS imaging of the brain in rodents due to its ability to provide a stable, long-lasting state of sedation, while preserving essential physiological functions [55, 56, 57]. Ketamine acts as a dissociative anesthetic, primarily inhibiting NMDA receptors, while xylazine, an alpha-2 adrenergic agonist, provides muscle relaxation and analgesia. This combination minimizes changes of the animal's vital functions and blood circulation, which is crucial
246 for fUS imaging, as the technique relies on detecting changes in CBF and hemodynamic responses to
247 infer neural activity. Moreover, ketamine/xylazine anesthesia reduces motion artifacts, providing
248 clearer and more reliable imaging data during fUS procedures. However, careful monitoring of the
249 depth of anesthesia is required since both agents can influence cardiovascular parameters, which may
250 affect blood flow measurements and, consequently, the interpretation of functional brain activity.

251 Ferrier et al. [58] studied the impact of another alpha-2 adrenergic agonist anesthetic (medetomidine)
252 on functional ultrasound connectivity during spontaneous brain activity in rodents and highlighted
253 how depth of anesthesia could affect the functional connectivity matrix.

254 Insulin resistance is pivotal to metabolic syndrome: all body and metabolic markers show a significant
255 correlation to insulin resistance in human adolescents [59]. It is associated with impairment in cardio-
256 and cerebrovascular function [5, 44, 60]. In our experiment, insulin resistance is detected very early, 3
257 weeks after the beginning of WD, which fits with previous publications reporting a very fast impact of
258 a hypercaloric diet on insulin sensitivity [39, 61]. This pattern is consistent with the deep impairment
259 of cerebrovascular function in Zucker diabetic and insulin-resistant rats [62]. Interestingly, a
260 neutraceutical approach with enriched DHA not only improves beta cell function in the periphery but
261 decreases insulin resistance in preclinic models and human adolescents [34, 63] and our results are in
262 favor of a recovery of insulin sensitivity by DHA supplementation resulting in a balanced ω-3:ω-6 PUFAs
263 ratio.

264 Several key molecules and pathways can be implicated in the link between obesity and impaired
265 cerebrovascular activity that we have shown in this article. Among the candidates, lactate plays a key
266 role in mediating obesity-induced insulin resistance via modulating inflammation in adipose tissue [64].

267 It is also present at high concentrations in the brain where its complex regulation between astrocytes
268 and neurons could also participate in insulin resistance. For example, inhibiting astrocyte lactic acid
269 production resulted in attenuated glucose-induced inflammatory cytokines in the hypothalamus [65].

270 More directly linked to insulin sensitivity is the insulin receptor substrate (IRS) protein family such as
271 IRS-1 which plays key roles in regulating metabolism and aging. For example, TNF-α, a proinflammatory cytokine produced by adipocytes, increases the serine/threonine phosphorylation of IRS-1, which may block insulin signaling and finally lead to the occurrence of insulin resistance [66]. Interestingly, lactate is associated with insulin resistance by the phosphorylation of IRS-1 in human skeletal muscle [67]. Quantifying systemic and brain lactate levels together with IRS-1 activity in our DIO model could be very useful to pinpoint molecular targets to prevent the impairment of CBF in obesity.

277 Another important parameter that would be important to follow in our DIO model is glucose uptake. Glucose metabolism and vascular regulation are tightly linked in the brain. Glucose uptake was shown to be downregulated in the brain of patients with obesity at younger ages [68] but the opposite result was also demonstrated with an increased insulin-stimulated brain glucose uptake in adult obesity [69]. Thus, glucose uptake should be examined in follow-up studies of our model.

282 Personalized nutrition for the prevention of T2D is among the new tools to pediatricians and pediatric diabetologists [70]. A higher intake of ω-3 PUFAs may be beneficial in the management of obesity [71,72], for cardiac and brain vascular function [35], especially in adolescents [73]. Long-chain ω-3 PUFAs fulfill crucial structural and functional roles in the body, notably in the human brain [74], and low ω-6:ω-3 PUFAs ratio is important for proper brain development in young ages [31]. In contrast, ω-6 PUFAs are pro-inflammatory [75], with higher intakes being linked to increased incidence of obesity and metabolic diseases [76]. One efficient way to restore a balanced ω-3:ω-6 PUFAs ratio is to use DHA supplementation in the WD and new nutritional methods include the use of ω-3 enriched diets [77] as complementary approaches to pharmacological treatments against obesity and obesity-related health risks. Using this nutritional approach, we show an improvement in all metabolic parameters as well as sensory-evoked CBV responses in obese mice. Our data complement and strength in the context of adolescent obesity previous observations in adults that ω-3-PUFA-enriched diets [78, 79], and particularly DHA supplementation, is beneficial to brain vascular function [33, 35, 73]. Similarly, to adult obesity, preventing weight gain during child development is the best strategy to avoid any
296 overweight-related disorders. However, once overweight is settled in adolescents, increasing ω-3
297 PUFA contents in the diet could be the first beneficial approach for cerebrovascular protection. Of
298 note, in our experimental conditions, metabolic improvement induced by a balanced ω-3:ω-6 PUFAs
299 ratio was not accompanied by weight loss. Thus, in our model, DHA is not likely to influence peripheral
300 lipotoxicity directly but rather to contribute to local protective mechanisms in the brain [80].

301 In conclusion, thanks to the high spatiotemporal resolution of fUS, we could efficiently sample
302 functional hyperemia in intact mice at different ages. fUS made it possible to i) show a mild decrease
303 in functional hyperemia in adolescent and middle-aged lean mice as previously shown in murine
304 models [81] and clinics [82] ii) demonstrate for the first time an early impairment of sensory-evoked
305 CBV response in adolescent obese and prediabetic mice similar to what was described in several brain
306 pathologies [16]. Further work is necessary to pinpoint the molecular/cellular sources of deficient
307 functional hyperemia in mice fed a WD since adolescence. Nonetheless, the beneficial nutraceutical
308 approach using a balanced ω-3:ω-6 PUFAs ratio by DHA supplementation to restore functional
309 hyperemia could efficiently decrease WD toxicity on metabolic homeostasis and cerebrovascular
310 activity.

311
312 Methods

313 Ethical approval

314 All animal experiments were approved by the committee for animal care of Université Paris Cité and
315 by the French Ministry of Research (agreement #17629) under the European directive 2010/63/UE.

316
317 **Experimental groups.** Male C57BL6/J mice were hosted in animal housing starting at 5 weeks of age.
318 After one week of habituation with all mice under regular diet (RD, Safe A04 diet, 2791kCal/kg, 10%
319 fat, 3,8% sugar), i.e. at six weeks of age (in the rodent peri-adolescence [84]), half were shifted to a
320 mixed RD and high fat-high sucrose diet (Western Diet (WD), Safe HF230 diet, 5317 kcal/kg, 60% fat,
321 27% sugar). Full composition of diets is indicated in Supplementary Table 1. To follow the aging effect
322 on WD mice and their control age-matched RD mice, metabolic phenotyping and imaging were
323 performed in 5 groups of animals at 1, 2, and 3 weeks of WD, then 2 months (adult mice) and 10
324 months (middle-aged mice) of diet. This means that the ages of the animals in these groups were
325 respectively 7, 8, and 9 weeks then 3,5 (adult) and 11,5 months (middle age). For the sake of clarity,
326 we used the terms 1W, 2W, 3W, 2M, and 10M for all mice to indicate the WD duration (Fig. 1a). To
327 study the effects of a balanced ω-3: ω-6 polyunsaturated fatty acids ratio (PUFAs, n-6:n-3=1), a WD
328 diet supplemented with docosahexaenoic acid (DHA) was provided to mice starting at 6 weeks of age
329 (WD +DHA group). For the WD used here, 5% DHA is needed to restore a proper ω-3:ω-6 ratio in PUFA
330 content of the WD (Safe diet).

331
332 **Metabolic Phenotyping.** A total of 51 C57BL/6J male mice were used for metabolic phenotyping of all
333 groups. Mice underwent Echo MRI body mass composition (EchoMRI-900 Whole Body Composition
334 Analyzers, Echo Medical Systems, Houston, TX). Body weight was measured weekly throughout the
335 study. Animals were accustomed to daily manipulation. Fasting blood glucose was assessed after
336 overnight fasting via the tail vein using Glucofix® Tech (A.Menarini Diagnostics, Rungis, France). Oral
337 glucose tolerance tests (OGTT) were performed after 5 hours of fasting to evaluate the impact of WD
338 on glycemia. Mice received glucose (1g/kg of body weight) by oral gavage and glycemia was monitored
339 using Glucofix® Tech for 120 min. Plasma insulin levels were quantified using a Christal Chem ELISA
340 assay from blood samples taken during the test. Insulin tolerance tests (ITT) were performed after 5
341 hours of fasting (0.5 UI/ kg of body weight, Novo-Nordisk, La Défense, France) and glycemia was
342 measured until 45 min, following the same protocol as OGTT.

343

344 Animal preparation for functional US (fUS) imaging. fUS acquisitions were performed on a total of
345 106 C57BL/6J male mice. Animals were anesthetized via intramuscular injection of a ketamine
346 chlorydrate (80 mg.kg-1) and xylazine (16 mg.kg-1) cocktail. The anesthesia level was adjusted, if
347 necessary, throughout the experiment. The body temperature was controlled by a rectal probe and
348 maintained at 37 ± 0.5 °C by a feedback-controlled heating pad.

349 Whisker Stimulation. Since the power Doppler signal is proportional to CBV [85], we could measure
350 neuronal activation in the barrel cortex during whisker stimulation through neurovascular coupling.
351 An acquisition consists of a 60s baseline followed by five successive trials. Each trial consists of 30s of
352 whisker stimulation at 2Hz, followed by 60s of recovery (no stimulation). 2Hz mechanical stimulation
353 is an optimal regime for overall whisker stimulation and has the advantage to avoid sensory
354 habituation. A custom-made mechanical whisker stimulator was built using a servo-motor controlled
355 by an Arduino Uno microcontroller interfaced with the imaging system through a TTL signal to
356 synchronize the stimulation pattern with the imaging sequence.

357 Ultrasound Sequence. After anesthesia, the animal was placed on a stereotaxic frame to stabilize its
358 head. Ultrasound imaging acquisitions were performed through the intact skin and skull with a 15-MHz
359 ultrasound probe (128 elements, 15 MHz central frequency, 100 x 100 µm2 spatial resolution, Vermon,
360 Tours, France). The probe was placed upon the brain with acoustic coupling gel to image the coronal
plane at Bregma – 1.70 mm comprising the barrel cortex. The transducer was connected to an ultrafast
ultrasound scanner (Iconeus One, Iconeus, France / Inserm Accelerator of Technological Research,
Paris, France). Data were acquired by emitting groups of 11 plane waves continuously tilted from -10°
to +10° at 5.5 kHz pulse repetition frequency. The backscattered echoes of each group were summed
to get compound images at a 500 Hz framerate that allows correct sampling of the blood signal.
Doppler images were computed from blocks of 200 compound images averaged after appropriate
filtering (see "Data Processing" section). fUS programming of custom transmit/receive ultrasound
sequences was done in Matlab (version R2021b, MathWorks, USA), using the software-based
architecture of the scanner.

**Data Processing of fUS.** Noise and tissue motion were discarded from the Doppler signal with a
spatiotemporal clutter filter based on the singular value decomposition (SVD) of the ultrasound raw
data. After removing the signal associated with the 60 first singular values, the time fluctuations of the
remaining Doppler signal were incoherently averaged to get the final Power Doppler signal,
proportional to the cerebral blood volume (CBV). Finally, a single Power Doppler image was computed
from 200 compound images every 400 ms. Each pixel of the final Power Doppler images is 100 x 100
µm2 in plane. The slice thickness is 300 µm. Filtered Power Doppler images of each animal were
registered to a reference mouse and linearly detrended in the spatial and temporal domains. For each
mouse, we computed the relative CBV (rCBV) time course, to get rid of the inter-individual variability
of cerebral perfusion. We defined the rCBV as the percentage change from the initial 60 s baseline.

**Activation Maps.** For each animal, activation maps were computed with a single-subject general linear
model (GLM) approach as described elsewhere [86]. Data analysis was performed using Matlab
(version R2021b, MathWorks, USA). The design matrix used to detect cortical activation specific to the
whisker stimulation only included the stimulus pattern as a regression signal. Statistical parametric
maps were generated for each session, including a z-map, a p-value map, a stimulation map, and a
baseline map. Individual delta cerebral blood volume (ΔCBV) maps were obtained by dividing the
stimulation matrix by the baseline matrix. To determine which pixels achieved statistical significance considering the comparison of all the pixels in the z-map, we considered p-value < 0.05 statistically significant and further performed Bonferroni correction for multiple tests comparison. The pixels meeting the defined criteria were considered activated. In functional neuroimaging, the z-score is used to quantify how much a given data point differs from the mean of the data, in terms of standard deviations [24]. The z-score represents the number of standard deviations of a voxel's signal intensity from the mean brain activity during a task. It takes into account the dispersion of the data within each group. Here, when each mouse is subjected to whisker stimuli, z-scores help assess whether the observed changes in brain activation are statistically significant, compared to the baseline. For each animal, an activation map displaying the activated pixels' z-score and a delta CBV map displaying the delta CBV of the same pixels were produced and overlaid onto the corresponding grayscale mean Doppler image. Mean activation maps were obtained after registration and averaging of these activation maps within groups.

**Functional Hemodynamic Response.** For each animal, the CBV time course was computed from a 7\*7 pixels box centered on the maximal z-score pixel of the activation map, after applying a median filter to the z-map. The pixel times series of the box were averaged spatially and temporally to get one mean trial per session (i.e. per animal). Then, mean trials were averaged within groups for comparison between WD and age-matched controls at different stages and statistical analysis. The final rCBV values for statistical analysis correspond to the mean percentage change of the last 20 s of stimulation (meaning at the maximum plateau from 10 to 30s of stimulation) to the initial 60 s baseline.

**Statistics.** Results of metabolic phenotyping, and whisker-based texture discrimination task are expressed as the Mean ± standard error of the mean. After the validation of normal distribution by a Shapiro-Wilk test, statistical analysis for intergroup comparison was performed in GraphPad Prism (software version 9.1.2) using the following tests: metabolic phenotyping, two-way ANOVA (for the
411 first set of experiments) or one-way ANOVA (for the second set dealing with DHA) followed by post-hoc Bonferroni test for two-by-two comparisons. Differences between groups are considered significant when the probability level is less than 5% (p-value<0.05), except for multiple comparisons where the p-value was adjusted as required. In the figures, the degrees of significance are indicated by * p-value<0.05; ** p-value<0.01; *** p-value<0.001; **** p-value<0.0001.

416

417 **Data availability.** All data generated or analysed during this study are included in this published article (and its supplementary information files). Detailed individual analysis will be made available upon request pending application and approval from the corresponding authors.

420
421
422
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639
640 Acknowledgments

641 We thank the animal core facility "Buffon" of the University of Paris Cité / Jacques Monod Institute for
642 Animal Care.

643
644 Author contributions

645 H.S. and C.M. equally contributed to the work. They designed the experiments, acquired and analyzed
646 the imaging data along with M.M. for metabolic phenotyping. F.P. and C.M. edited the article. M.T.
647 and H.G. equally contributed to the work. They designed the experiments and wrote the article
648 together with H.S., C.M., M.M., F.P., and C.M., M.T. and H.G. supervised the project.

649
650 Funding
651 H.S. had a postdoctoral fellowship award from NIH and AXA. M.M. had a doctoral fellowship award

652 from Idex Université Paris Cité. This work was supported by the AXA Research Fund (M.T.).

653

654 **Competing interests**

655 The authors declare no competing interests.

656
657

658

## Main figure titles and legends

659

660

Fig. 1 Glucose metabolism in mice fed a WD at adolescence is altered from 3 weeks to 10 months of diet compared to age-matched RD mice.

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(a) Experimental groups. Abbreviations indicate the duration of the diet. For example, 1W corresponds to 1 week of WD. Since diet starts at 6 weeks of age (adolescence), 1W group corresponds to 7 week-aged mice. Note that the WD leading to obesity is a mixture of RD and a high-fat high-sucrose diet. (b-i) Metabolic phenotyping of obese mice fed a WD for a duration of 1, 2, 3 weeks or 2, 10 months (1W, 2W, 3W, 2M, and 10M groups) and their respective age-matched control mice groups fed with RD. (b) Body weight, (c) fat and lean mass, and (d) overnight fasting blood glucose. (e) Area under the curve (AUC) of blood glucose during oral glucose tolerance tests (OGTT) and (f) inverse AUC of blood glucose during insulin tolerance tests (ITT). (g-i), Plasma insulin levels from blood samples taken during the OGTT test in d. Note that the threshold of metabolic dysfunction starts at 3 weeks of WD. Metabolic data are represented as mean ± SEM and were acquired on the same mice for each diet group at a given age, n=6 per diet group at a given age. P values were calculated using two-way ANOVA with post hoc Bonferroni tests. \*P<0.05, \*\*P<0.01, \*\*\*P<0.001, and \*\*\*\*P<0.0001. a.u., arbitrary units; ns, not significant.

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Fig. 2 Impaired functional hyperemia to whisker stimulations in adolescent, adult, and middle-aged mice fed a WD since adolescence compared to age-matched RD mice.

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(a) Experimental setup. The intact skull and intact skin of the head-fixed anesthetized mouse are insonified in a coronal plane continuously with a 15 MHz central frequency ultrasound probe during mechanical whisker stimulation in the anesthetized mouse. (b) fUS imaging of the somatosensory-induced activity at Bregma -1.7 mm with superimposition of the activated pixels (color scale) over the

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681
682 brain vasculature (greyscale). The mean activation map shows significantly activated pixels in the left
683 barrel cortex area following stimulation of right whiskers with a custom-made mechanical stimulator.
684 Background map centered on the barrel cortex coordinates [83]. (c) Time course changes of relative
685 CBV (rCBV) signal. Left: time course changes of rCBV as a percentage of baseline in the barrel cortex
686 (ΔrCBV(%)). The total recording time for a single trial was 510 s, with a 60 s baseline, a 30 s stimulation,
687 and a 60 s recovery time. 5 consecutive whisker stimulation trials were acquired (curves in
688 transparency define Standard Deviation, n = 11 RD mice). Right: mean time course changes of
689 ΔrCBV(%) averaged over 5 trials (mean ± SD, n = 11, from (c)). The rise slope of the fUS signal and ΔrCBV
690 at the maximum plateau of the vascular response are locked to whisker-stimulation onset. (d)
691 Activation z-score map. Same representation as (b) for all diet duration groups. (e) Mean z-score (f)
692 ΔrCBV maps. (g) Mean variation of relative CBV (%ΔrCBV) values are represented over responding
693 pixels in response to five stimulation trials. Color intensity goes from dark blue (no change during
694 stimulation) to red (strong CBV increase during the 30 s stimulation). Mean %ΔrCBV averaged at the
695 maximum plateau between 10 s and 30 s stimulation from (h). Scale bar in (a-b) 2 mm. (h) Time course
696 of rCBV signal. Whisker stimulation-evoked variation in ΔrCBV is plotted as a percentage change of
697 baseline (%ΔrCBV). Note the plateau of maximum %ΔrCBV between 10 s and 30 s of whisker
698 stimulation.

699

700 **Fig. 3 Functional hyperemia dynamics during mouse development are different in lean and obese**
701 **mice.**

702 (a) Aging effect on the rise slope of CBV time course represented in Fig 2g for RD groups (left panel)
703 and WD groups (right panel). The rise slopes of the CBV time course signal were calculated on the first
704 5 s of sensory stimulation. RD mice: 2.8 ± 0.48% of CBV increase per second in 1W group (n=5), 1.9 ±
705 0.22 in 2W group (n=5), 1.2 ± 0.14 in 3W group (n=9), 1.2 ± 0.2 in 2M group (n=7) and 1.1 ± 0.13 for
706 10M group (n=8). For WD-fed mice: 2.5 ± 0.31% of CBV increase per second in 1W group (n=8), 2.2 ±
0.2 in 2W group (n=8), 1.0 ± 0.16 in 3W group (n=9), 1.0 ± 0.13 in 2M group (n=11) and 0.9 ± 0.18 in 10M group (n=6). (b) Aging effect on the mean CBV calculated from time course in Fig 2g for RD groups (left panel) and WD groups (right panel). Data points are the same as in fig 2h. (c) Comparison of RD and WD groups at different ages. Data points are the same as in (b). (d) Beginning WD in adolescence not adulthood triggers profound impairment in functional hyperemia. Left: Right:

Results in (a-g) are presented as mean ± SD for 1W (n = 5 for RD mice, n = 8 for WD mice), 2W (n = 5 RD, n = 8 WD), 3W (n = 9 RD, n = 9 WD), 2M (n = 7 RD, n = 11 WD) and for 10M group (n = 8 RD, n = 6 WD). Mean P values were calculated using ANOVA for intragroup aging effects with posthoc Bonferroni's multiple comparisons test and unpaired t-test to compare RD vs WD at different ages.

\*P<0.05, \*\*P<0.01, \*\*\*P<0.001, and \*\*\*\*P<0.0001.

<caption>Fig. 4 Impairment in glucose metabolism in obese mice due to WD is canceled by feeding with WD balanced in the ω-3:ω-6 PUFAs ratio</caption>

Metabolic phenotyping of adult RD-fed versus adult obese mice fed either WD or WD+DHA for 2 months starting at adolescence. (a) Body weight, (b) fat and lean mass, (c) overnight fasting blood glucose, (d) AUC of blood glucose during OGTT, and (e) inverse AUC of blood glucose during insulin ITT. (f) Plasma insulin levels from blood samples taken during the OGTT test in (d). (g-i) same analysis and representation for fUS imaging as Fig.2. c-d. Data are represented as mean ± SEM and were acquired on the same mice from each diet group, n=7 per diet group. P values were calculated using two-way ANOVA with post hoc Bonferroni tests. \*P<0.05, \*\*P<0.01, \*\*\*P<0.001, and \*\*\*\*P<0.0001. a.u., arbitrary units; ns, not significant.

<caption>Fig. 5 The amplitude of functional hyperemia is restored in adult WD-fed mice with a balanced ω-3:ω-6 PUFAs ratio</caption>
731 (a) Activation map. Same representation that Fig. 2d for RD, WD, and WD+DHA groups. (b) ΔrCBV map.

732 Same representation as Fig. 2e for RD, WD, and WD+DHA groups. (c) Averaged time course changes

733 of relative CBV as a percentage of baseline in the barrel cortex (ΔrCBV(%)) for RD, WD, and WD+DHA

734 groups. (d) Number of the activated pixels recorded in the activation map (quantified as Z-scores). The

735 mean Z-score is 8.11 ± 0.36 in RD-fed mice (n = 8), 7.40 ± 0.48 in WD-fed mice (n=7) and 8.44 ± 0.33 in

736 WD+DHA-fed mice (n=6). (e) Mean %ΔrCBV at maximum plateau (10-30 s of whisker stimulation).

737 Mean values are 11.56 ± 2.9 for RD-fed mice (n=8), 7.2 ± 2.6 for WD-fed mice (n=7) and 10.37 ± 3.4

738 for WD+DHA-fed mice (n=6). Data are presented as mean ± SD. P values are calculated using an

739 unpaired t-test. P<0.05, **P<0.01, ***P<0.001, and ****P<0.0001.

740
a Control mice

<figure><img src="image_3.png" /></figure>

Regular Diet

<figure><img src="image_4.png" /></figure>

Regular Diet (RD)
+ High-Fat-High-Sucrose Diet (HFHSD)

<table><thead><tr><th>Groups</th><th>WD duration</th><th>Age</th></tr></thead><tbody><tr><td>1W group</td><td>1-week-WD</td><td>7-weeks-old</td></tr><tr><td>2W group</td><td>2-weeks-WD</td><td>8-weeks-old</td></tr><tr><td>3W group</td><td>3-weeks-WD</td><td>9-weeks-old</td></tr><tr><td>2M group</td><td>2-months-WD</td><td>4-months-old</td></tr><tr><td>10M group</td><td>10-months-WD</td><td>12-months-old</td></tr></tbody></table>

b Body weight

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

c Body composition

<figure><img src="image_6.png" /></figure>

d Fasting blood Glucose

<figure><img src="image_7.png" /></figure>

e Blood glucose
OGTT 1g/kg

<figure><img src="image_8.png" /></figure>

f Blood glucose
ITT 0.5UI/kg

<figure><img src="image_9.png" /></figure>

g 1W : Insulinemia
during OGTT

<figure><img src="image_10.png" /></figure>

h 2W : Insulinemia
during OGTT

<figure><img src="image_11.png" /></figure>

i 3W : Insulinemia
during OGTT

<figure><img src="image_12.png" /></figure>

j 2M : Insulinemia
during OGTT

<figure><img src="image_13.png" /></figure>

k 10M : Insulinemia
during OGTT

<figure><img src="image_14.png" /></figure>
<figure><img src="image_15.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_16.png" /></figure>

Coronal
slice

<figure><img src="image_17.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_18.png" /></figure>

Stimulation pattern : 30s ON – 60s OFF

<figure><img src="image_19.png" /><figcaption>d Z-score Map</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>e mean Z-score</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>f ΔrCBV Map</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>g %ΔrCBV</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>h Time course of rCBV</figcaption></figure>

<figure><img src="image_24.png" /></figure>
a. Rise slope

<figure><img src="image_25.png" /></figure>

b. mean %ΔrCBV

c. mean% ΔrCBV
RD versus WD

<figure><img src="image_26.png" /></figure>

d. Early vs late onset of WD

<figure><img src="image_27.png" /></figure>
<figure><img src="image_28.png" /></figure>
a Activation Map

<figure><img src="image_29.png" /></figure>

b ΔrCBV Map

<figure><img src="image_30.png" /></figure>

c Time course of rCBV signal

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

d mean Z Scores

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

e mean %ΔrCBV

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>