Saffron extract interferes with lipopolysaccharide-induced brain activation of the kynurenine pathway and impairment of monoamine neurotransmission in mice

BackgroundAlthough activation of inflammatory processes is essential to fight infections, its prolonged impact on brain function is well known to contribute to the pathophysiology of many medical conditions, including neuropsychiatric disorders. Therefore, identifying novel strategies to selectively counter the harmful effects of neuroinflammation appears as a major health concern. In that context, this study aimed to test the relevance of a nutritional intervention with saffron, a spice known for centuries for its beneficial effect on health.MethodsFor this purpose, the impact of an acute oral administration of a standardized saffron extract, which was previously shown to display neuromodulatory properties and reduce depressive-like behavior, was measured in mice challenged with lipopolysaccharide (LPS, 830 μg/kg, ip).ResultsPretreatment with saffron extract (6.5 mg/kg, per os) did not reduce LPS-induced sickness behavior, preserving therefore this adaptive behavioral response essential for host defense. However, it interfered with delayed changes of expression of cytokines, chemokines and markers of microglial activation measured 24 h post-LPS treatment in key brain areas for behavior and mood control (frontal cortex, hippocampus, striatum). Importantly, this pretreatment also counteracted by that time the impact of LPS on several neurobiological processes contributing to inflammation-induced emotional alterations, in particular the activation of the kynurenine pathway, assessed through the expression of its main enzymes, as well as concomitant impairment of serotonergic and dopaminergic neurotransmission.ConclusionAltogether, this study provides important clues on how saffron extract interferes with brain function in conditions of immune stimulation and supports the relevance of saffron-based nutritional interventions to improve the management of inflammation-related comorbidities

Microbiota and Metabolite Profiling as Markers of Mood Disorders: A Cross-Sectional Study in Obese Patients

Obesity is associated with an increased risk of several neurological and psychiatric diseases, but few studies report the contribution of biological features in the occurrence of mood disorders in obese patients. The aim of the study is to evaluate the potential links between serum metabolomics and gut microbiome, and mood disturbances in a cohort of obese patients. Psychological, biological characteristics and nutritional habits were evaluated in 94 obese subjects from the Food4Gut study stratified according to their mood score assessed by the Positive and Negative Affect Schedule (PANAS). The fecal gut microbiota and plasma non-targeted metabolomics were analysed. Obese subjects with increased negative mood display elevated levels of Coprococcus as well as decreased levels of Sutterella and Lactobacillus. Serum metabolite profile analysis reveals in these subjects altered levels of several amino acid-derived metabolites, such as an increased level of L-histidine and a decreased in phenylacetylglutamine, linked to altered gut microbiota composition and function rather than to differences in dietary amino acid intake. Regarding clinical profile, we did not observe any differences between both groups. Our results reveal new microbiota-derived metabolites that characterize the alterations of mood in obese subjects, thereby allowing to propose new targets to tackle mood disturbances in this context. Food4gut, clinicaltrial.gov: NCT03852069

Gut microbiota, biological and psychological alterations in alcohol use disorder

Alcohol use disorder (AUD) is a major public health problem affecting 5 to 10% of the population. Chronic alcohol abuse induces alterations in the composition of the gut microbiota, which are correlated with psychological symptoms, suggesting the involvement of the gut-brain axis in the development of addiction. Among dietary component able to modulate the microbiota, dietary fibers are of particular interest. In a first study we investigated the link between dietary fiber intake and psychological symptoms in AUD patients. We found that only a proportion of AUD patients displays alterations in the gut microbiota composition. This dysbiosis is associated with higher craving scores and impaired sociability. Finally, our intervention study aiming to supplement AUD patients with inulin shows that 1) it does not lead to gastro-intestinal intolerance, 2) it induces specific changes in the gut microbiota, 3) it has limited impact on biological and behavioural outcomes, 4) it increases sociability score. If our results show the importance of prebiotic dietary fiber in AUD patients, further studies are needed to define an adapted strategy targeting the gut microbiota to improve metabolic and behavioural alterations in these patients.(BIFA - Sciences biomédicales et pharmaceutiques) -- UCL, 202

Improving the acceptability to enhance the efficiency of stroke rehabilitation procedures based on brain-computer interfaces: General public results

International audienceStroke leaves around 40% of surviving patients dependent in their activities of daily living, notably due to severe motor disabilities [Inserm, 2019]. Brain-Computer Interfaces (BCIs) have been shown to be efficient for improving motor recovery after stroke [Cervera et al., 2018], but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified, they are insufficient: fully optimised BCIs are pointless if patients and clinicians do not want to use them [Blain-Moraes et al., 2012].We hypothesise that improving BCI acceptability and acceptance, by better informing stakeholders about BCI functioning and by personalising the BCI-based rehabilitation procedures to each patient, respectively, will favour engagement in the rehabilitation process and result in an increased efficiency.Our first objective was to identify the factors influencing the intention to use (IU) BCIs [Davis, 1989]. Based on the literature, we constructed a model of BCI acceptability and adapted it in questionnaires addressed to the general population (n=753) and post-stroke patients (n=33). Videos were included, one about the general functioning of BCIs, the second about their relevance for rehabilitation.We used random forest algorithms to explain IU based on our model's factors. After the first video, IU was mainly explained by subjective and personal factors, i.e., perceived usefulness (PU), perceived ease of use (PEOU) and BCI playfulness for the general population, and PU, autonomy and engagement in the rehabilitation for the patients. After the second video, the explanatory factors became more scientific/rational, with PU, cost-benefits ratio and scientific relevance for the general population, and PU, scientific relevance and ease of learning for patients.The shift of main explanatory factors (before/after second video) from subjective representations to scientific arguments highlights the impact of providing patients with clear information regarding BCIs

Improving the acceptability to enhance the efficiency of stroke rehabilitation procedures based on brain-computer interfaces: General public results

International audienceStroke leaves around 40% of surviving patients dependent in their activities of daily living, notably due to severe motor disabilities [Inserm, 2019]. Brain-Computer Interfaces (BCIs) have been shown to be efficient for improving motor recovery after stroke [Cervera et al., 2018], but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified, they are insufficient: fully optimised BCIs are pointless if patients and clinicians do not want to use them [Blain-Moraes et al., 2012].We hypothesise that improving BCI acceptability and acceptance, by better informing stakeholders about BCI functioning and by personalising the BCI-based rehabilitation procedures to each patient, respectively, will favour engagement in the rehabilitation process and result in an increased efficiency.Our first objective was to identify the factors influencing the intention to use (IU) BCIs [Davis, 1989]. Based on the literature, we constructed a model of BCI acceptability and adapted it in questionnaires addressed to the general population (n=753) and post-stroke patients (n=33). Videos were included, one about the general functioning of BCIs, the second about their relevance for rehabilitation.We used random forest algorithms to explain IU based on our model's factors. After the first video, IU was mainly explained by subjective and personal factors, i.e., perceived usefulness (PU), perceived ease of use (PEOU) and BCI playfulness for the general population, and PU, autonomy and engagement in the rehabilitation for the patients. After the second video, the explanatory factors became more scientific/rational, with PU, cost-benefits ratio and scientific relevance for the general population, and PU, scientific relevance and ease of learning for patients.The shift of main explanatory factors (before/after second video) from subjective representations to scientific arguments highlights the impact of providing patients with clear information regarding BCIs

[What is the role of the gut microbiota in the development of alcohol use disorders?]

Alcohol addiction is a complex and multifactorial disease influenced by social, psychological and biological aspects. The current pharmacological drugs used in the management of alcohol dependence have shown only a modest efficacy and the relapse rate remains high in this disease. Recently, the gut microbiota, a huge and dynamic ecosystem made up of billions of microorganisms living in our intestine, has been shown to regulate many important functions for human health. Indeed, the gut microbiota is known to influence our metabolism, our immune system as well as our nervous system with consequences for brain functions, mood and behaviour. We have shown that heavy and chronic alcohol consumption induced important changes in the composition of the gut microbiota. Furthermore, the microbial changes are associated with the severity of depression, anxiety and alcohol craving that are important factors predicting the risk of relapse. This suggests the existence of a gut-brain axis in alcohol dependence and supports the development of new therapeutic alternatives, targeting the gut microbiota, in the management of alcohol dependence

Modelling the Acceptance of BCI-based Stroke Rehabilitation Procedures: Heading for Efficiently Personalised Therapies

International audienc

Troubles liés à l’usage d’alcool : et si l’addiction trouvait son origine dans l’intestin ?

L’addiction à l’alcool est une maladie complexe, impliquant à la fois des facteurs sociaux, psychologiques et biologiques. La prise en charge des patients alcoolo-dépendants est difficile car les médicaments actuels ont une efficacité limitée dans le maintien de l’abstinence, et le taux de rechute reste très élevé. Récemment, le microbiote intestinal, un écosystème constitué de milliards de micro-organismes vivant dans notre intestin, est devenu un acteur clé de la santé humaine. Il est connu pour réguler notre métabolisme, notre système immunitaire, mais également notre système nerveux, et donc notre comportement et notre humeur. Nos études récentes ont montré que la consommation abusive d’alcool entraîne des modifications importantes de la composition du microbiote intestinal. Nous avons également montré que ces altérations microbiennes étaient associées à la sévérité des symptômes de dépression, d’anxiété et d’appétence à l’alcool, suggérant ainsi l’existence d’un dialogue entre l’intestin et le cerveau. Ces résultats encouragent la recherche de nouvelles pistes thérapeutiques, ciblant le microbiote intestinal, dans le traitement de la dépendance à l’alcool.[What is the role of the gut microbiota in the development of alcohol use disorders?]. Alcohol addiction is a complex and multifactorial disease influenced by social, psychological and biological aspects. The current pharmacological drugs used in the management of alcohol dependence have shown only a modest efficacy and the relapse rate remains high in this disease. Recently, the gut microbiota, a huge and dynamic ecosystem made up of billions of microorganisms living in our intestine, has been shown to regulate many important functions for human health. Indeed, the gut microbiota is known to influence our metabolism, our immune system as well as our nervous system with consequences for brain functions, mood and behaviour. We have shown that heavy and chronic alcohol consumption induced important changes in the composition of the gut microbiota. Furthermore, the microbial changes are associated with the severity of depression, anxiety and alcohol craving that are important factors predicting the risk of relapse. This suggests the existence of a gut-brain axis in alcohol dependence and supports the development of new therapeutic alternatives, targeting the gut microbiota, in the management of alcohol dependence

Identifying profiles of patients to personalise BCI-based procedures for motor rehabilitation after stroke

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140).Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient.Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) “scientific relevance” & “ease of learning”; (c2) “benefits/risks ratio”, “ease of learning”, “visual aesthetic” & “result demonstrability”; (c3) “scientific relevance” & “benefits/risks ratio”;(c4) none.Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one

Etudier l’acceptabilité des interfaces cerveau-ordinateur en rééducation motrice post-AVC pour proposer des protocoles personnalisés en fonction du profil du patient

International audienceMalgré leur intérêt, les interfaces cerveau-ordinateur (ICO) ne sont pas utilisées en soins courants dans le domaine de la rééducation post-accident vasculaire cérébral. Nous faisons l’hypothèse que l’amélioration de l’acceptabilité des ICO, obtenue par une personnalisation des protocoles, permettra aux patients d’être moins anxieux et plus engagés, et ainsi d’optimiser l’efficacité en termes de récupération motrice, mais aussi l’utilisabilité.Nous avons conçu un questionnaire basé sur notre modèle théorique d’acceptabilité des ICO, auquel 140 sujets post-AVC ont répondu. Une identification des profils de sujets avec une analyse en composante principale (ACP) puis une clusterisation (méthode Elbow et K-mean clustering) a été réalisée. Un arbre de classification a été construit pour classer les nouveaux sujets dans leur cluster. Il a été validé par « leave-one-out cross validation » (LOOV). Pour chaque cluster, nous avons effectué des régressions et des corrélations pour identifier les facteurs à personnaliser.L’ACP a permis de passer de 27 à 15 facteurs, à partir desquels nous avons obtenu 5 clusters (Fig.1A, 1B). La performance de classification en LOOV était de 65,00 % (niveau de hasard pour α=5% : 30,75%) (Fig. 1C). Les facteurs d’importance majeurs sont (N = nombre de clusters les intégrant) : pertinence scientifique (4), facilité d’apprentissage (3), balance bénéfices/risques (2), esthétique et démonstrabilité (1). Ces résultats sont intégrés dans un logiciel « plug&play » utilisable en soins courants.Un essai contrôlé randomisé multicentrique permettra d’évaluer et d’optimiser les protocoles personnalisés proposés afin que les ICO soient plus utilisables/acceptables pour les patients et les soignants