Non-dysbiotic gut microbiota profiles are associated with different spontaneous cortical activity in healthy older people

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Ignacio Serrano, Silvia Cruz-Gil, Cristina M. Fernández, Lara P. Fernández, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6226318/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract The gut and the brain have shown to be linked through a multimodal, bidirectional pathway called the gut-brain axis. In the gut-to-brain way, the gut microbiota has been shown to be the main factor. The physiological and biochemical mechanisms of this communication have been described in detail, and the gut microbiota has been shown to be altered in many neurological and psychiatric conditions. However, it is unknown how the gut microbiota influences the brain activity, especially in the healthy condition. By clustering healthy older people by their corresponding microbiome and comparing their spontaneous cortical activity, we demonstrate that different unaltered microbiota profiles are associated with different spontaneous activity in medial posterior cortical areas. These areas are associated to memory, language and emotion processing abilities. Therefore, we provide evidence that normal gut microbiota profiles modulate spontaneous cortical activity related to cognitive functions that typically decline with age. This implies that nutritional interventions that modify microbiota composition could help delay or ameliorate natural age-related cognitive decline. Biological sciences/Neuroscience/Cognitive neuroscience Biological sciences/Microbiology/Communities/Microbiome Biological sciences/Neuroscience/Cognitive ageing Biological sciences/Physiology/Neurophysiology Biological sciences/Physiology/Ageing Health sciences/Gastroenterology/Gastrointestinal system/Microbiota Gut-brain axis Gut microbiota profiles Spontaneous cortical activity EEG source density Healthy older population Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction During the last decade, a bidirectional relationship between the brain and the intestines through what is called gut-brain axis has been widely evidenced. In this regard, the gut microbiota has been shown to be altered in many neurological conditions. However, does the undisturbed gut microbiota modulate the spontaneous brain activity in healthy conditions in any way? The gut microbiota is the set of microorganisms that reside in the intestinal environment. The gut microbiota is considered an organ of the gastrointestinal tract (Senchukova, 2023 ) that provides additional genes and functions to the genetic resources of the human species and is involved in multiple physiological processes (nutrition, development, immunity, etc.). Several essential functions carried out by the microbiota, such as the transformation of non-digestible food components into absorbable metabolites, the synthesis of essential vitamins, the elimination of toxic compounds, the strengthening of the intestinal barrier or the regulation of the immune system, demonstrate its importance (Hou et al., 2022 ). Human gut microbiota presents an enormous diversity. It matures and changes throughout our life cycles (childhood, adolescence, adulthood, old age), and is a fundamental element to protecting our health. From birth, and throughout the first three years of life, the gut microbiota rapidly increases in diversity until the adult stage is reached. Subsequently, there is a stable community with dominant and subdominant bacterial species, although the abundance of different bacteria may fluctuate in response to external factors (diet, medications, travel, etc.) (Tochitani, 2021 ). Thus, gut microbiota has particularities and characteristics specific to each individual, and may vary according to genetic endowment, diet, drugs, geography and interaction with the environment (Cryan et al., 2019 ). In order to identify patterns related to both human healthy and pathologic states, a series of reproducible profiles of gut microbiome variation in healthy adults have been established, which are composed of a number of characteristic bacterial groupings known as enterotypes. Three normality enterotypes have been proposed, based on the co-occurrence of different genus of microorganisms where one genus stands out from the rest (Arumugam et al., 2011 ; Costea et al., 2018 ): enterotype 1, being Bacteroides the prevalent indicator; enterotype 2, led by Prevotella; and enterotype 3, with abundance of Ruminococcus. Any other combination of maladaptive and pathogenic microorganisms together with a decreased biodiversity can be considered intestinal dysbiosis. The dysbiosis involves an imbalance in the ratio of the number and types of bacteria that disrupts the microbial ecosystem to the point of exceeding its resilience (Sanchís and Cortés, 2021 ). Associations have been also found between intestinal dysbiosis and diseases such as diabetes (Haluzík et al., 2018 ), cancer (Amitay et al., 2018 ), or inflammatory bowel disease (Eom et al., 2018 ), among others. In recent years, the number of preclinical and clinical studies that find relationships between pathological changes in the intestinal microbiota and neurological (Parkinson's disease, Alzheimer's disease) and mental (depression, stress, autism) diseases has grown significantly (Bosch et al., 2022 ; Dogra et al., 2022 ; Radjabzadeh et al., 2022 ; Tarawneh and Penhos, 2022 ; Fernández-Calvet et al., 2024 ), giving rise to the theory that a balanced microbiota is essential to maintain a healthy state. All these diseases share, in a certain extent, chronic inflammation likely resulting from microbial dysbiosis, which points out to the intestinal microbiota as a key factor in the onset of these disorders (Spielman et al., 2018 ). The idea that the gut microbiota may have significant impact on the central nervous system and neuroinflammation and, consequently, on neurological and behavioral disorders, is framed within the gut-brain axis research domain. This axis represents the bidirectional pathway of communication between the gut microbiota and the central nervous system (CNS). It consists of several major routes (Banerjee et al., 2023 ): 1) the immune system, which is activated by the microbiota and releases cytokines that circulate in the bloodstream and influence the CNS through the blood-brain barrier; 2) the neuroendoncrine system by regulating tryptophan metabolism and neurotransmitter production such as glutamate, butyrate, dopamine, serotonin and gamma aminobutyric acid (GABA), which are key neurotransmitters in many conditions; and 3) the vagus nerve (VN), that connects the intestinal nervous system to the CNS and the brain. By any of these vias, the intestinal microbiota seems to be a fundamental modulator of this axis (Fülling et al., 2019 ). Although the specific mechanisms of these bidirectional communication routes between the intestinal microbiota and the brain are still unraveled (Cryan and Mazmanian, 2022 ), the VN represents perhaps the most direct and fastest neural communication link between the gut microbiota and the brain (Han et al. 2022 ). There are preclinical and clinical evidences on the role of the VN in inflammatory processes that could contribute to the development of certain neurodegenerative diseases as Parkinson’s disease (Greene, 2014 ) and mental disorders affecting emotion and cognition, such as depression (Inserra et al., 2018 ) or autism spectrum disorder (Sgritta et al., 2019 ). The VN is the longest cranial nerve with the widest innervation of body organs and it is part of the parasympathetic branch of the autonomic nervous system. It contains nearly 80% of afferent fibers, devoted to transmitting sensory information to the CNS from body organs while the remaining 20% efferent fibers carry movement information to regulate the function of different viscera including the gut (Bonaz et al., 2019). The afferent endings of the VN are located in the muscular layer and in the mucosa of the intestine, some of which synapse with neurons of the enteric nervous system, which amplify the range of signals that can be transmitted by the VN (Han et al., 2022 ). VN afferents have an electrophysiological response to the presence of cytokines, nutrients, peptides and hormones in the epithelial layer of the intestine. In addition, some bacteria can produce neurotransmitters such as serotonin which, when absorbed, can act directly on the VN endings or, indirectly, through activation of enteric neurons (Kaelberer et al., 2018 ). Thus, either mechanically or chemically, the gut microbiota is involved in VN signaling. All this raises the question of whether imbalance in the gut microbiota, which affects brain and behavior, as evidenced by the supplementation of bacterial species or transferring gut microbiota of patients with depression or Alzheimer’s Disease to germ-free animals (Allen et al., 2016 ; Grabucker et al., 2023), induces neurobiological changes either by modifying vagus nerve signaling, by slowly altering the properties of the signal or both. Changes in excitability, remodeling of synapses or sensitivity can permanently affect VN signaling and change its tone, i.e., modify the strength of vagal activation in its dynamic interaction with the parasympathetic nervous system and thus modify the properties of the VN and the neural networks on which it influences (Fülling et al., 2019 ). On the other hand, the vagus nerve stimulation (VNS) has demonstrated therapeutic benefits in diseases such as epilepsy and depression (Broncel et al., 2020 ). The modulation of the VN, through stimulation and modification of the signal it transmits, may be the mechanism used by the gut microbiota to activate VN afferents and reach the brain. Several studies on the neurobiology that seems to be involved in the response to VNS have revealed the existence of a neural circuit that can be considered as the afferent brain network of the VN (Hachem et al., 2018 ), i.e., the network formed by the subcortical and cortical brain areas to which the VN transmits its signal. The application of fMRI and PET techniques in human clinical studies have revealed the structural networks linked to the VN and the relationships among different elements of this vagal afferent network. The visceral afferent fibers contained in the VN join to the nucleus tractus solitarious (NTS) (Yakunina et al., 2017 ). The neural pathway connecting the NTS with the locus coeruleus (LC) and the dorsal raphe nucleus (DRN), all brainstem structures, opens the way for the VN to modulate the activity of areas such as the thalamus, the limbic system and the cerebral cortex (Yakunina et al., 2018 ). Based on the scientific evidence that gut microbiota can modify the VN signal and this, in turn, can be reflected in brain activity and behavior, the present exploratory study aims to determine whether different microbiota composition produces different spontaneous brain activity in healthy older participants, and whether that brain activity might be related to neurodegenerative diseases and cognitive or emotional disorders before they become apparent. Positive findings would facilitate and promote further research on preventing, delaying or attenuating the onset of these disorders through simple nutritional interventions that modulate the gut microbiota. Table 1 Average (standard deviation)/Percentage (#) values of the sociodemographic variables of the participants in the three microbiota-based clusters and the statistics of the main effect. BMI: Body mass index; MMSE: Minimental State Examination; DASS: Depression Anxiety Stress Scales; COS: Oviedo Sleep Quality Questionnaire. Cluster A N = 26 Cluster B N = 22 Cluster C N = 6 Statistic Age 62.35 (6.04) 62.00 (5.24) 60.50 (6.19) F(2,51) = .252, p = .778 Sex (female) 53.85% (14) 68.18% (15) 66.67% (4) χ 2 (2) = 1.118, p = .572 BMI 27.08 (5.59) 26.55 (3.28) 25.90 (4.79) F(2,51) = .181, p = .835 MMSE 34.08 (1.79) 35.18 (5.02) 33.33 (1.97) F(2,51) = .928, p = .402 DASS Depression 1.27 (1.85) 2.27 (3.40) .50 (.55) F(2,51) = 1.555, p = .221 DASS Anxiety 1.15 (1.62) 1.64 (1.81) .50 (.84) F(2,51) = 1.267, p = .290 DASS Stress 2.92 (2.51) 4.55 (3.78) 2.00 (1.41) F(2,51) = 2.520, p = .090 DASS Total 5.35 (4.69) 8.45 (7.78) 3.00 (2.37) F(2,51) = 2.633, p = .082 COS Subjective Satisfaction 4.46 (1.53) 4.32 (1.64) 4.67 (1.51) F(2,51) = .128, p = .880 COS Insomnia 17.54 (5.41) 18.36 (6.46) 15.33 (6.19) F(2,51) = .620, p = .542 COS Hypersomnia 4.31 (1.72) 4.09 (2.02) 3.83 (1.17) F(2,51) = .201, p = .819 COS Total 26.31 (5.39) 26.77 (6.11) 23.83 (6.97) F(2,51) = .598, p = .554 Results Microbiota-based clusters Three clusters were identified by the X-Means clustering algorithm (A, B, and C) based on the microbiota relative population (%) at the genus level, composed of 26, 22 and 6 participants, respectively. Table 1 shows the average (standard deviation) values of the demographic and depression-related variables, together with the statistics of the differences between them. In this sense, the clusters did not present any significant difference in age, sex, body mass index, depression- and anxiety-related, and scores between them. To test the influence of the X-Means criterion of the optimum number of clusters on the resultant distribution, the algorithm was also executed with fixed numbers of clusters of 2 and 4. In the former case, clusters A and B were grouped as one while cluster C remained the same. In the latter case, cluster A was split into two subclusters and clusters B and C remained the same. Therefore, the inner distribution was kept regardless the number of clusters. Microbiota differences between microbiota-based clusters Figure 1a and Table S1 show the bacteria genera that presented significant statistical differences between clusters. According to Table S2, cluster B had a higher proportion of Bacteroides than clusters A and C. Besides, the proportion of Prevotella 9, Mycobacterium, Holdemanella, Coriobacteriaceae UCG-003, Libanicoccus, Lactiplantibacilus, Luenostoc and Stenotrophomonas is higher in cluster C than in the other two clusters. Cluster B contained a higher proportion of Barnesiella and Ruminococcus (gnavus group) than cluster A, whereas cluster A presents a higher proportion of Marvinbryantia and other genera than cluster B. Summarizing (Fig. 1b), the microbiota of cluster C is mainly composed of Prevotella 9 and then Bacteroides in an inverse relation. The microbiota of cluster A is mainly composed of Bacteroides and then Prevotella 9. Despite the microbiota of cluster B is mainly composed of Bacteroides too (significantly higher than in the other two clusters), the second most abundant bacteria genus in this cluster is not Prevotella, like in cluster A, but Barnesiella. Moreover, the presence of Mycobacterium, Leuconostoc and Stenotrophomonas was evident in cluster C, in contrast to clusters A and B. However, cluster A and B evidenced the presence of Ruminococcus (gnavus group), in contrast to cluster C. According to the three enterotypes defined by (Arumugam et al., 2011; Costea et al., 2018), the gut microbial composition of the clusters A and B found in the current study fits to the enterotype 1, since the genus Bacteroides is the most abundant and cluster C meets the criteria defining enterotype 2, where Prevotella is the most abundant and is inversely correlated with Bacteroides. Spontaneous cortical activity differences between microbiota-based clusters Tables S2 and S3 show the cortical areas of the Brainnetome atlas that presented a statistically significant main effect of cluster in eyes closed and eyes open conditions, respectively. The spontaneous brain activity between clusters A and B (Fig. 2) only differs significantly in the right inferior frontal sulcus, Brodmann areas (BAs) 9 and 46, in the low alpha band (B more active than A), in the eyes open conditions. The differences of brain activity between cluster C, and clusters A and B were very similar (Fig. 3 and Fig. 4). Cluster C showed a higher activity than clusters A and B in the posterior cingulate cortex and the precuneus, bilaterally, and the left fusiform gyrus in the theta band during the eyes closed condition. During the eyes open condition, only the left posterior cingulate area and the left precuneus presented more activation in cluster C than in clusters A and B. The particular difference between the brain activation of clusters C and B (Fig. 4) is the higher activity of cluster B in the right superior primary motor cortex (M1) in the beta band, during the eyes open condition. Cognitive functions associated to the areas with activity differences between clusters Figure 5 shows the activation likelihood estimation (ALE, Eickhoff et al., 2009) of the areas that significantly differed between clusters A and B (Fig. 5a), A and C and B and C (Fig. 5b), respectively during cognitive tasks according to brainmap.org. The only area significantly different in spontaneous activity between clusters A and B (Fig. 5b) is mainly activated during pain perception and working memory usage. With respect to the areas with different activity between clusters A-B and C (Fig. 5b), they are also mostly related to episodic memory, language and social cognition. However, other areas are also related to action perception and processing, cognition and emotion processing. This analysis reveals differences in memory, language, emotion processing and action skills partly due to microbiota composition. Linear modelling of brain ROIs activity from microbiota Linear models of the sum of ROIs activity in the theta band eyes closed (Fig. 3b and Fig. 4b), theta band eyes open (Fig. 3a, and Fig. 4a), alpha band eyes open (Fig. 2), and beta band eyes open (Fig. 4a, bottom left) were constructed. The only significant model found was the one for the sum of the source density in the theta band eyes closed, adjusted R 2 = .846, F(13) = 16.945, p < .0005. Figure 6 shows the scatterplot ( a ) of the model fitting and the relative contribution of the predictors to the model ( b ). All the predictors contributed significantly to the model (p < .05). The linear model indicates that the source density in the theta band during eyes closed condition can be estimated from some bacterial genera abundance. The most contributing genus to the model was Prevotella 9, which in turn presented significant differences between clusters A and C, and B and C. This result points to the relative abundance of Prevotella 9 as one of the sources of the difference in brain activity in the identified ROIs between clusters. Discussion In this article, a study on healthy subjects is presented in which the result of the analysis of their gut microbiota showed three groups with statistically significant differences in composition and abundance. The analysis of their spontaneous electroencephalographical activity also showed significant differences between subjects in the three groups. Based on the scientific evidence that gut microbiota can modify the VN signal and this, in turn, can be reflected in brain activity and behavior, the hypothesis underlying the findings of this study on the observed differences in brain activity is that the different groups of gut microbiota modulated differently the VN signal and, through it, they were responsible for the specific brain activity found in each group. As said before, the VN can transmit signals between the brain and peripheral organs in a bidirectional way. One of the key mechanisms to encode sensory information in the gut are by primary vagal afferent neurons, which are mechanical and chemical sensitive to gut stimuli or condition (Mayer, 2011 ; Wang et al., 2021 ). Accordingly, these terminals can detect and reply to gut muscular tone, inflammation or bacterial composition (Cryan et al., 2019 ) by expressing different classes of receptors in an adaptive way depending on the homeostatic state of the host (Mayer, 2011 ). From the anatomical point of view, VN afferent fibers from the gut contact bilaterally with the nucleus tractus solitarius (NTS) (Broncel et al., 2020 ). The caudal nucleus of the NTS projects the sensory information contained in the electrical signal of the VN to the brain and receives information from different brain areas. Thus, NTS can be seen as an integrating central of these two organs (Holt 2022 ). There are multiple evidences of the structural and functional connections from NTS to other close brainstem structures and distant cortical regions (Forstenpointner et al., 2022 ). From a MRI-based research of NTS, the authors established a conceptual map of connectivity among the NTS and the brainstem, the subcortex, the cortex and the cerebellum. Regarding cortical regions, the NTS connects with the insular cortex, medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), mid-cingulate cortex (MCC), and primary somatosensory cortex (S1). Other works about the role of the NTS are mainly based on the study of the VN stimulation (VNS) effects. A model of plausible vagal pathways implicated in the brain activity by VNS is proposed in (Briand et al., 2020 ), founded on several different noninvasive transcutaneous VNS (tVNS) fMRI studies on healthy people. According to this model, once VNS activates the structures of the lower brainstem as NTS, this one transmits activation to upper brainstem areas as the LC and DRN. The activation of LC produces norepinephrine that modulates large-scale brain networks involved in cognitive functions like memory, attention, emotion or stress reaction. At the cortical level, this pathway may reconfigure the activity of the insula, the dorsolateral and dorsomedial prefrontal cortices and the sensory cortex. Furthermore, the activation of DRN produces serotonin, which selectively affects to brain regions of the limbic system as the ACC, the posterior cingulate cortex (PCC) and the dorsomedial prefrontal cortex (dmPFC). Most of these areas have been involved in our study. In line with this model, a review of several studies contained in (Broncel et al., 2020 ) also describes how the vagal stimulation applied to, either healthy and neurological impaired people, impacts in the LC activity increasing the release of neurotransmitters as the norepinephrine, which is involved in most of the structures related to memory. Besides, the norepinephrine, projected by the LC to the cerebral cortex, stimulates the receptors of the DRN, which projects serotonin to the cerebral cortex. Both neurotransmitters have neuroprotective effects for the brain and modulate neurogenesis (Cheyuo et al., 2011). Another anatomical model dealing with the modulation of the brain activity and functional connectivity by tVNS is proposed in (Liu et al., 2020 ). The model collects the evidences on neuroimaging biomarkers obtained from different studies with healthy participants. The stimulation of VN changes the activation in the insula, precentral gyrus, PCC, ACC, prefrontal and frontal cortices, S1 and parahippocampal gyrus, as it happens in our case. On the other hand, in people with major depressive disorder (MDD), the treatment with VNS produces changes in functional connectivity between the posterior default mode network (DMN) (precuneus and PCC) and the emotional and reward structures (insula and parahippocampus), between the posterior and anterior (ACC, nucleus accumbens and ventromedial prefrontal cortex) DMN, between the anterior DMN and the dorsomedial and dorsolateral prefrontal cortices, and between dorsomedial and dorsolateral prefrontal cortices and emotional and reward structures. This work also highlights the anti-inflammatory role of VNS in ameliorating depressive symptoms in some subtypes of MDD since the VN can be considered as an interface among neurotransmitters, neuroendocrine system, neuroinflammation and gut-related immunity. VNS has also been studied in healthy people to find its influence on cognitive functions by analysing the neural pathways engaged in VNS action. Most of these studies focuses on the prefrontal cortex and hippocampus and on the role of neurotransmitters as norepinephrine, serotonin and GABA (Klaming et al., 2022 ) in cognition and emotion. In (Zhang et al., 2022 ), synchronized fMRI and tVNS were acquired to analyse the effects of VNS on brain activity and cognitive performance. This work showed that VNS results on a better performance on memory and language skills and produces correlated significant spontaneous brain activity changes on the calcarine gyrus, fusiform gyrus, lingual gyrus, and parahippocampal gyrus. In addition, it has been widely evidenced that the left fusiform gyrus is involved in the emotion processing of faces (Monroe et al., 2013 ; Jung et al., 2021 ) Since the transcutaneous stimulation of the vagus nerve enhances recognition of emotions in faces (Sellaro et al., 2018 ), the differences in source density in that area found in our study are plausibly explained by vagus nerve path, which is signaled in the gut. In older healthy people, tVNS improves associative memory performance based on the hypothesis that tVNS modulates the memory using similar mechanisms to how emotion affects memory: emotional arousal increases the level of adrenalin that activates afferent VN fibers, and these activate the LC, which releases noradrenalin to the hippocampus and the amygdala that are brain areas involved in memory formation and consolidation (Jacobs et al., 2015 ). Other neuroimaging studies revisited in (Zhang et al., 2022 ) point to the precuneus as a cortical brain area involved in visuospatial imagery, contextual memory and information processing, among others, and to the parahippocampal gyrus as the main cortical input to the hippocampus. Both regions are significantly activated when tVNS is applied, as can be seen in our study. The concomitant increase of norepinephrine concentration in prefrontal cortex following tVNS has also demonstrated its effects on inhibitory control processes modulated by working memory load (Beste et al., 2016). In the same way, tVNS raises GABA levels and both neurotransmitters enhance attention and executive functions (Klaming et al., 2022 ). Caution is required when interpreting the results of our study. The assumption that different gut microbiota composition leads to signal specific brain cortical areas in a distinct way mediated by the vagus nerve does not necessarily imply that gut microbiota is the sole cause of the brain activity differences. It is necessary to keep on making studies that include more factors that can have impact on gut microbiota differences as pointed out in (Cryan and Mazmanian, 2022 ). Although there were not found significant differences in the MMSE and DASS21 tests administered among people included in the three microbiota clusters, since they are all healthy participants, the differences in cortical activity in the areas found for the different microbiota clusters suggest that the microbiota may modulate some cognitive abilities likely to be impaired with age in the absence of other neurological and neuropsychiatric pathologies. The main areas that presented differences in spontaneous activity between microbiota-based clusters in our study, the precuneus and the posterior cingulate cortex, are considered as a central hub part of the posterior default mode network (DMN) (Utevsky, Smith and Huettel, 2014 ). In the absence of pathology, these areas have been traditionally related to functions such as visuo-spatial imagery, episodic memory and self-processing (Cavanna and Trimble, 2006 ), although the participation in working memory, reward and fear processing has been also recently evidenced (Dadario and Sughrue, 2023 ). However, given their widespread functional and structural connections and their high relative metabolic rate (Leech and Sharp, 2013 ), any disorder on them might lead to a variety of cognitive changes (Kumral, Bayam and Özdemir, 2021 ). As a matter of fact, the precuneus and PCC have been pointed as key vulnerable areas for several neuropathologies and psychiatric disorders (Dadario and Sughrue, 2023 ). With regard to our study, a psychiatric disorder has been shown to concur with an increase in resting metabolism in the precuneus and PCC (as cluster C in our study): major depressive disorder (MDD) (Leech and Sharp, 2014). Since participants ultimately included in the analysis did not present symptoms of depression according to the DASS test, and all cluster were homogeneous in this sense, MDD can be discarded as the cause of the differences in spontaneous activity found between clusters in the precuneus and PCC. Besides, the area that presented difference in spontaneous activity between clusters A and B, the right inferior frontal sulcus (IFS), BAs 9 and 46, is mainly implicated in working memory and differences in cortical thickness due to age were evidenced to correlate with working memory performance (Eich, Lao and Anderson, 2021 ). Given also that the IFS is an area differentially affected by ageing (Feng et al., 2020 ), the difference in microbiota between clusters A and B points to a difference in healthy brain ageing (despite no difference in chronological age). Again, this needs further research. Summarizing, this paper presents the first study to assess the relation between gut microbiota and spontaneous brain activity measured by resting-state EEG in healthy senior people. We have reported two main findings: 1) gut microbiota composition could impact on brain activity, which has been demonstrated by two procedures, based on a cluster analysis and on a predictive model; and 2) the brain activity could influence the working memory aging process. Methods Participant recruitment and sample collection The recruitment was performed through the GENYAL Clinical Trials Platform at IMDEA Food Institute. This study was approved by the institutional Research Ethics Committee (protocol ID: IMD PI-055, IMDEA Food Foundation) and performed in accordance with the principles of research involving human subjects stated in the Declaration of Helsinki (1964) and Good Clinical Practice (CGP). All participants were clearly informed about the study methodology and provided written informed consent. Anthropometric measurements, peripheral blood samples and fecal samples were collected from a total of fifty-four healthy participants over 55 years of age (Male: 21, Female: 33). Sampling and data acquisition were performed between October and November 2022. Inclusion criteria included: Age ≥ 55 years; BMI between 27–35 kg/m2; able to understand the informed consent; willing to comply with the study protocol. Exclusion criteria included: decreased cognitive function, pregnancy or breastfeeding, severe chronic health conditions (heart, liver, etc.), BMI > 35 kg/m2, or pharmacological treatment such as anxiolytics, antidepressants, medication for sleep disorders, or any type of psychotropic medication. Anthropometric Measurements Anthropometric measurements were collected following standardized methodology. Body Composition Monitor analyzer (BF511- OMRON HEALTHCARE UK, LT, Kyoto, Japan) was used to determine weight, % body fat, % skeletal muscle and visceral fat, as well as basal metabolic rate. Height was measured with a stadiometer (Leicester-Biological Medical Technology SL, Barcelona). Waist and hip circumference (cm) were measured using Seca 201 (Quirumed, Valencia, Spain). Blood pressure and pulse were monitored with the Model M3 blood pressure monitor from OMRON HEALTHCARE UK, LT, Kyoto, Japan). Behavioral measures: sleep habits (OSQ questionnaire), emotional state (DASS-21 questionnaire) and mental state (MMSE questionnaire) OSQ Sleep habits were assessed via the Oviedo Sleep Questionnaire (OSQ) (Bobes et al., 1998 ). The OSQ is a validated interview that support diagnosis of insomnia and hypersomnia during the previous month based on the DSM-IV and ED-10 criteria. The questionnaire is made up of a total of 15 items including Subjective satisfaction with sleep, Insomnia (difficulty with falling or remaining asleep, early awakenings or restorative sleep, among others), hypersomnia (daytime sleepiness and its effects on daily tasks). DASS-21 The psychological state of the participants was measured using the short version of the Depression, Anxiety and Stress Scale (DASS-21) (Antony et al., 1998 ). designed to assess the emotional states of depression, anxiety, and stress. The DASS-21 consists of 21 items, divided into three subscales of 7 Likert items. A score for each scale is calculated by summing all the related items and these scores are later added to obtain a general score. MMSE The Mini-Mental State Examination (MMSE) (Spanish version) is a widely utilized cognitive screening tool designed to assess cognitive impairment and dementia (Folstein et al., 1975 ; Lobo et al., 1979 ). The MMSE evaluate five cognitive domains: orientation, registration, attention and calculation, recall, and language. The total score has a maximum punctuation of 35, with lower scores indicating greater cognitive impairment. Fecal samples processing and 16S sequencing Sample Collection and DNA Extraction Fecal samples were sent to Novogene for 16S sequencing of the bacterial V3V4 region: DNA was extracted by using Magnetic Soil and Stool DNA Kit (TianGen, China, Catalog #: DP712), following the manufacturer's protocol to ensure high-quality and high-yield DNA suitable for downstream applications. PCR Amplification of 16S rRNA Gene The 16S rRNA gene was amplified using a set of universal primers targeting the V3-V4 regions of the 16S rRNA gene. The primers used were (5'- CCTAYGGGRBGCASCAG-3') and (5'- GGACTACNNGGGTATCTAAT-3'), which are known for their broad coverage of bacterial taxa while minimizing amplification of eukaryotic rRNA genes. The PCR conditions were optimized to ensure specific amplification, with an initial denaturation at 98°C for 1 minute, followed by 30 cycles of denaturation at 98°C for 10 seconds, annealing at 50°C for 30 seconds, and extension at 72°C for 30 seconds, with a final extension at 72°C for 5 minutes. High-Throughput Sequencing The amplified 16S rRNA gene fragments were performed by using specific primers connecting with barcodes. The PCR products of proper size were selected through 2% agarose gel electrophoresis. The same amount of PCR products from each sample was pooled, end-repaired, A-tailed, and further ligated with Illumina adapters. Libraries were sequenced on a paired-end Illumina platform to generate 250bp paired-end raw reads. Data Processing and Analysis Raw sequencing data were processed using the DADA2 pipeline, which includes quality filtering, dereplication, chimera removal, and sequence variant inference. This method allows for the recovery of full-length 16S rRNA gene sequences with single-nucleotide resolution and a near-zero error rate, ensuring high accuracy in microbial community profiling. Operational Taxonomic Unit (OTU) Clustering Sequences were clustered into Operational Taxonomic Units (OTUs) using a de novo clustering approach, which has been shown to outperform reference-based methods in terms of stability and quality of OTU assignments. The clustering was performed using the VSEARCH algorithm, which is a viable open-source alternative to USEARCH. Taxonomic Assignment Taxonomic classification of the OTUs was performed using the SILVA database, which provides comprehensive and up-to-date taxonomic information for 16S rRNA gene sequences. The classification was done at various taxonomic levels, including phylum, class, order, family, genus, and species. EEG Data Acquisition The participants were comfortably seated with their forearms resting in their thighs, one meter away from a white wall. They were asked to relax and breathe deeply with their mouth naturally open during three-minute periods, first with eyes closed and then with eyes open staring at the wall, with a one-minute resting interval between the two conditions. The EEG signal was acquired from 32 wet active ActiCAP electrodes (Brain Products GmbH, Gilching, Germany) placed on the scalp in the positions Fp1, Fp2, AF3, AF4, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, PO3, PO4, O1, Oz, O2 according to the 10–20 system. Reference was set to the Fz electrode and ground to the Fpz electrode. The signal was digitized and recorded by a actiCHamp amplifier (Brain Products GmbH, Gilching, Germany) at 256Hz. The impedance of all electrodes was kept under 10 kΩ. EEG Processing The EEG signal was preprocessed offline by a custom script in Matlab R2019b (The MathWorks Inc., Natick, MA, USA) using the EEGLAB (Delorme & Makeig, 2004 ) functions. The signal was first cleaned by using Artifact Subspace Reconstruction (ASR) (Kothe & Makeig, 2013 ), rejecting burst intervals above a threshold of 20 standard deviations (all the other parameters set to default). Then, the signal was bandpass filtered between 3Hz and 31Hz. After that, eye- and muscle- related artifacts were removed by IClabel (Pion-Tonachi et al., 2019) with thresholds above 70% of probability. Next, bad channels were interpolated using the default parameters of the PREP pipeline (Bigdely-Shamlo et al., 2015 ). Finally, the signal was rereferenced to the average value among all channels. Source density in six frequency bands (theta: 4Hz-7Hz; low alpha: 7Hz-10Hz; high alpha: 10Hz-13Hz; low beta: 13Hz-18Hz; mid beta: 18.5Hz-21Hz; high beta: 21Hz-30Hz) was calculated from the preprocessed signal by the eLORETA algorithm (Pascual-Marqui et al., 2011 ) implemented in the LORETA-KEY software v20221229 (KEY Institute for Brain-Mind Research, Zurich, Switzerland). Source density was averaged for each of the 210 cortical areas defined by the Brainnetome atlas (Fan et al., 2016 ). Data and statistical analysis Participants were clustered from their microbiota population (at genus level) by the X-Means algorithm (Pelleg & Moore, 2000 ), which automatically determines the optimum number of clusters (maximum intra-cluster, minimum inter-cluster similarity) based on the Bayesian Information Criterion (BIC). Differences in socio-demographic variables and microbiota population were tested by MANOVA tests, after confirmation of normality with the Shapiro-Wilk test and Q-Q plots. Post-hoc pairwise comparisons were performed with Tukey’s method after confirmation of significant main effect of cluster. Differences in categorical variables were tested by the Chi-squared test. Significance was considered with p < .05. Effect size was reported as partial eta squared. Differences in EEG source densities between clusters were tested by Kruskal-Wallis test, after confirmation of non-normality by the Shapiro-Wilk test and Q-Q plots. Significance was considered with p < .01 in this case. Post-hoc pairwise comparisons were performed with Bonferroni’s correction after confirmation of significant main effect of cluster. All the above analyses and tests were performed with SPSS v29.0.2.0 (Armonk, NY: IBM Corp.). Finally, linear regression models were applied to predict the source density sum of the ROIs that presented significant differences between clusters. For that purpose, the automatic linear modelling procedure of the SPPS v29.0.2.0 (Armonk, NY: IBM Corp.) was applied, setting the number of predictors to the number of bacteria genera that presented significant differences between clusters, and Akaike Information Criterion (AIC) as model fit index. Models with p < .05 (ANOVA) were considered statistically significant. Importance of each predictor was calculated as the residual sum of squares with the predictor removed from the model, normalized so that all the importance values sum up to 1. Declarations Acknowledgements This work was supported by grants PID2023-150271NB-C21 and PID2023-150271NB-C22 funded by MICIU/AEI/ 10.13039/501100011033 (Spanish Ministry of Science, Innovation and University, Spanish State Research Agency). Other grants involved were PID2022-138295OB-I00 funded by MICIU/AEI/ 10.13039/501100011033, RTC2019-007294-1 funded by MICIU/AEI/ 10.13039/501100011033 and “FEDER/UE”, and PIE202350E170 funded by the Spanish National Research Council. Also supported by Regional Government of Community of Madrid (NutriSION-CM Y2020/BIO-6350). The authors thank the volunteers for participation and commitment in the study. Competing interests The authors declare no competing interests. Author contributions J.I.S.: conceptualization, methodology, software, verification, formal analysis, investigation, data curation, writing – original draft, visualization; S.C.-G.: conceptualization, methodology, investigation, resources, data curation, writing -original draft, visualization; C.M.F.: conceptualization, methodology, investigation, resources, data curation, writing -original draft; L.P.F.: conceptualization, methodology, investigation, resources; A.S.: software, investigation, writing- review & editing; I.E.-S.: conceptualization, methodology, investigation, resources; C.M.: conceptualization, methodology, investigation, resources; M.-J.L.: conceptualization, methodology, funding acquisition; M.C.: validation, writing – review & editing, visualization; R.R.-R.: methodology, project administration, funding acquisition, supervision; A.R.d.M.: conceptualization, methodology, supervision; M.D.d.C.: conceptualization, methodology, verification, formal analysis, investigation, resources, writing – original draft, supervision. 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Yue Wang, Gaofeng Zhan, Ziwen Cai, Bo Jiao, Yilin Zhao, Shiyong Li, Ailin Luo, Vagus nerve stimulation in brain diseases: Therapeutic applications and biological mechanisms, Neuroscience & Biobehavioral Reviews, Volume 127, 2021, Pages 37-53, ISSN 0149-7634, https://doi.org/10.1016/j.neubiorev.2021.04.018. Zhang H, Guo Z, Qu Y, Zhao Y, Yang Y, Du J, Yang C. Cognitive function and brain activation before and after transcutaneous cervical vagus nerve stimulation in healthy adults: A concurrent tcVNS-fMRI study. Front Psychol. 2022 Nov 11;13:1003411. doi: 10.3389/fpsyg.2022.1003411. PMID: 36438376; PMCID: PMC9691850. Additional Declarations No competing interests reported. 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As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6226318","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440606494,"identity":"48dee5a7-8a2c-46dc-977e-53196228965b","order_by":0,"name":"J. Ignacio Serrano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACxmYwJYEQ4QfiA6RpkWwgoAUTGBBSz9zOfu3BjxoLOQb+w8c+V9TckTM+fvzhgR8MNva4HcZTbthzTMKYQSIteeaZY8+Mzc7kGBzsYUhLbMCtJU2Ct0EisUGCx5ixge1w4rYbPAyHGRgOJ+CxJU3yL0gL//nPjA3/DidunsH+AKjlPx6HsR+TBtvCkMPM2Nh2OHGDBIMBUMsBRjwOY5OWAfqFTSLNmLGx77CxBNgvBsk4/WLYf/yZ5JuaOjl+/sOPGRu+HZbjbz/++MOPCjucDjNs4DEAM9hQxQ1waWBgkGdgf4BbdhSMglEwCkYBCAAAIEFSb0GIW5UAAAAASUVORK5CYII=","orcid":"","institution":"Spanish National Research Council","correspondingAuthor":true,"prefix":"","firstName":"J.","middleName":"Ignacio","lastName":"Serrano","suffix":""},{"id":440606496,"identity":"b974c3ce-67a9-42f7-9ccf-47afd5cc4fab","order_by":1,"name":"Silvia Cruz-Gil","email":"","orcid":"","institution":"IMDEA Food Institute, CEI UAM+CSIC","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Cruz-Gil","suffix":""},{"id":440606498,"identity":"1c6ebe19-ab46-4a6c-a8e8-39fc14fe93d6","order_by":2,"name":"Cristina M. Fernández","email":"","orcid":"","institution":"IMDEA Food Institute, CEI UAM+CSIC","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"M.","lastName":"Fernández","suffix":""},{"id":440606500,"identity":"59d88262-b781-478c-b0c1-9c42707de87c","order_by":3,"name":"Lara P. Fernández","email":"","orcid":"","institution":"IMDEA Food Institute, CEI UAM+CSIC","correspondingAuthor":false,"prefix":"","firstName":"Lara","middleName":"P.","lastName":"Fernández","suffix":""},{"id":440606502,"identity":"3174d44f-61b4-4593-8be6-1857520d9ef3","order_by":4,"name":"Ana Sobrino","email":"","orcid":"","institution":"Spanish National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Sobrino","suffix":""},{"id":440606504,"identity":"f42e4df8-0650-4b7a-ad86-4b8fdd19bcfb","order_by":5,"name":"Isabel Espinosa-Salinas","email":"","orcid":"","institution":"IMDEA Food Institute, CEI UAM+CSIC","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Espinosa-Salinas","suffix":""},{"id":440606505,"identity":"a7c61319-6f74-4630-bbb1-3ee92f6ecb8d","order_by":6,"name":"Carolina Maestre","email":"","orcid":"","institution":"Centro Nacional de Investigaciones Oncológicas (CNIO)","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Maestre","suffix":""},{"id":440606506,"identity":"cab81f39-95d2-4cae-adb2-1511f21c43d1","order_by":7,"name":"María-Jesús Latasa","email":"","orcid":"","institution":"IMDEA Food Institute, CEI UAM+CSIC","correspondingAuthor":false,"prefix":"","firstName":"María-Jesús","middleName":"","lastName":"Latasa","suffix":""},{"id":440606507,"identity":"a95fc2c5-d16a-4542-92e6-9d4d405121ab","order_by":8,"name":"Manuel Cebrián","email":"","orcid":"","institution":"Spanish National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Cebrián","suffix":""},{"id":440606511,"identity":"151ebc59-e79d-41bc-927d-95e363e6c1ba","order_by":9,"name":"Ricardo Ramos-Ruiz","email":"","orcid":"","institution":"IMDEA Food Institute, CEI UAM+CSIC","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Ramos-Ruiz","suffix":""},{"id":440606514,"identity":"81da2961-ca70-4e2c-8cda-dedab9190b78","order_by":10,"name":"Ana Ramírez Molina","email":"","orcid":"","institution":"IMDEA Food Institute, CEI UAM+CSIC","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Ramírez","lastName":"Molina","suffix":""},{"id":440606517,"identity":"db785d58-8582-43fe-9a59-7094a6a3ce9c","order_by":11,"name":"M. Dolores Castillo","email":"","orcid":"","institution":"Spanish National Research Council","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Dolores","lastName":"Castillo","suffix":""}],"badges":[],"createdAt":"2025-03-14 12:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6226318/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6226318/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-16090-6","type":"published","date":"2025-08-27T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80302666,"identity":"5a7f8604-6f0a-404c-bc59-7d4942fb556f","added_by":"auto","created_at":"2025-04-10 09:37:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eAverage log relative occurrence (%) of the bacteria genera that presented statistically significant difference between clusters A, B and C after post-hoc correction. X-Y: Statistically significant post-hoc difference between clusters X and Y in the corresponding bacteria genus of the x-axis. \u003cstrong\u003eb\u003c/strong\u003e Microbiota diversity for the three clusters of the bacteria genera that presented statistically significant differences between them.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6226318/v1/7504a4cc21c8ff0dbc2c5de3.png"},{"id":80303335,"identity":"5522b7b3-567f-4c79-9b75-606bd4268a97","added_by":"auto","created_at":"2025-04-10 09:45:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99442,"visible":true,"origin":"","legend":"\u003cp\u003eP color-coded values of the statistically significant differences in normalized EEG source density between microbiota-based clusters A and B after post hoc correction, in eyes open condition for different brain areas (according to the Brainnetome atlas) and frequency bands. LH: Left hemisphere; RH: Right hemisphere.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6226318/v1/12ba5166f6bf40b1d18f9291.png"},{"id":80302671,"identity":"32029dbe-b637-4187-b329-4735abd042ac","added_by":"auto","created_at":"2025-04-10 09:37:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":338429,"visible":true,"origin":"","legend":"\u003cp\u003eP color-coded values of the statistically significant differences in normalized EEG source density between microbiota-based clusters A and C after post hoc correction, in eyes open (\u003cstrong\u003ea\u003c/strong\u003e) and eyes closed (\u003cstrong\u003eb\u003c/strong\u003e) conditions for different brain areas (according to the Brainnetome atlas) and different frequency bands. LH: Left hemisphere; RH: Right hemisphere.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6226318/v1/0fc609d5c46de02dd5829ab0.png"},{"id":80303324,"identity":"afc0a5b5-48c3-4e83-903a-c4f5c16da287","added_by":"auto","created_at":"2025-04-10 09:45:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":411230,"visible":true,"origin":"","legend":"\u003cp\u003eP color-coded values of the statistically significant differences in normalized EEG source density between microbiota-based clusters B and C after post hoc correction, in eyes open (\u003cstrong\u003ea\u003c/strong\u003e) and eyes closed (\u003cstrong\u003eb\u003c/strong\u003e) conditions for different brain areas (according to the Brainnetome atlas) and different frequency bands. LH: Left hemisphere; RH: Right hemisphere.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6226318/v1/a1bd849b58a9787f30c92592.png"},{"id":80302670,"identity":"93a54302-f698-474e-97ef-2d886f2bd6b5","added_by":"auto","created_at":"2025-04-10 09:37:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":225467,"visible":true,"origin":"","legend":"\u003cp\u003eColor-coded activation likelihood estimation (ALE) of the cortical areas (and average and number) that presented significant differences between \u003cstrong\u003ea \u003c/strong\u003eclusters A and B;\u003cstrong\u003e b \u003c/strong\u003eclusters A and C, and B and C, during the tasks according to the brainmap.org taxonomy.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6226318/v1/682aaa71f14789a4ea63e98f.png"},{"id":80302673,"identity":"35473208-a8ab-4b3a-826a-99328e7b07bd","added_by":"auto","created_at":"2025-04-10 09:37:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":175138,"visible":true,"origin":"","legend":"\u003cp\u003eLinear model of the sum of the source activity of the ROIs that presented significant differences between clusters in the theta band with eyes closed. \u003cstrong\u003ea\u003c/strong\u003e Model fit. \u003cstrong\u003eb\u003c/strong\u003e Relative contribution of each predictor to the model. Red negative bars indicate a negative coefficient not a negative importance index.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6226318/v1/11bcea4fb188f0ca3302bef4.png"},{"id":90344913,"identity":"c68bb6d3-73a0-4e15-a8fd-302624b14623","added_by":"auto","created_at":"2025-09-01 16:07:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2540669,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6226318/v1/fe385194-58f8-49aa-8f0c-282ed884cd1d.pdf"},{"id":80303323,"identity":"eea32b0a-d117-40f2-843d-b2939640fb17","added_by":"auto","created_at":"2025-04-10 09:45:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":98066,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6226318/v1/64cbc04220d83b31f727857d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-dysbiotic gut microbiota profiles are associated with different spontaneous cortical activity in healthy older people","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDuring the last decade, a bidirectional relationship between the brain and the intestines through what is called gut-brain axis has been widely evidenced. In this regard, the gut microbiota has been shown to be altered in many neurological conditions. However, does the undisturbed gut microbiota modulate the spontaneous brain activity in healthy conditions in any way?\u003c/p\u003e \u003cp\u003eThe gut microbiota is the set of microorganisms that reside in the intestinal environment. The gut microbiota is considered an organ of the gastrointestinal tract (Senchukova, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) that provides additional genes and functions to the genetic resources of the human species and is involved in multiple physiological processes (nutrition, development, immunity, etc.). Several essential functions carried out by the microbiota, such as the transformation of non-digestible food components into absorbable metabolites, the synthesis of essential vitamins, the elimination of toxic compounds, the strengthening of the intestinal barrier or the regulation of the immune system, demonstrate its importance (Hou et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHuman gut microbiota presents an enormous diversity. It matures and changes throughout our life cycles (childhood, adolescence, adulthood, old age), and is a fundamental element to protecting our health. From birth, and throughout the first three years of life, the gut microbiota rapidly increases in diversity until the adult stage is reached. Subsequently, there is a stable community with dominant and subdominant bacterial species, although the abundance of different bacteria may fluctuate in response to external factors (diet, medications, travel, etc.) (Tochitani, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, gut microbiota has particularities and characteristics specific to each individual, and may vary according to genetic endowment, diet, drugs, geography and interaction with the environment (Cryan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to identify patterns related to both human healthy and pathologic states, a series of reproducible profiles of gut microbiome variation in healthy adults have been established, which are composed of a number of characteristic bacterial groupings known as enterotypes. Three normality enterotypes have been proposed, based on the co-occurrence of different genus of microorganisms where one genus stands out from the rest (Arumugam et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Costea et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e): enterotype 1, being Bacteroides the prevalent indicator; enterotype 2, led by Prevotella; and enterotype 3, with abundance of Ruminococcus. Any other combination of maladaptive and pathogenic microorganisms together with a decreased biodiversity can be considered intestinal dysbiosis.\u003c/p\u003e \u003cp\u003eThe dysbiosis involves an imbalance in the ratio of the number and types of bacteria that disrupts the microbial ecosystem to the point of exceeding its resilience (Sanch\u0026iacute;s and Cort\u0026eacute;s, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Associations have been also found between intestinal dysbiosis and diseases such as diabetes (Haluz\u0026iacute;k et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), cancer (Amitay et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), or inflammatory bowel disease (Eom et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), among others. In recent years, the number of preclinical and clinical studies that find relationships between pathological changes in the intestinal microbiota and neurological (Parkinson's disease, Alzheimer's disease) and mental (depression, stress, autism) diseases has grown significantly (Bosch et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dogra et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Radjabzadeh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tarawneh and Penhos, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fern\u0026aacute;ndez-Calvet et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), giving rise to the theory that a balanced microbiota is essential to maintain a healthy state. All these diseases share, in a certain extent, chronic inflammation likely resulting from microbial dysbiosis, which points out to the intestinal microbiota as a key factor in the onset of these disorders (Spielman et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe idea that the gut microbiota may have significant impact on the central nervous system and neuroinflammation and, consequently, on neurological and behavioral disorders, is framed within the gut-brain axis research domain. This axis represents the bidirectional pathway of communication between the gut microbiota and the central nervous system (CNS). It consists of several major routes (Banerjee et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e): 1) the immune system, which is activated by the microbiota and releases cytokines that circulate in the bloodstream and influence the CNS through the blood-brain barrier; 2) the neuroendoncrine system by regulating tryptophan metabolism and neurotransmitter production such as glutamate, butyrate, dopamine, serotonin and gamma aminobutyric acid (GABA), which are key neurotransmitters in many conditions; and 3) the vagus nerve (VN), that connects the intestinal nervous system to the CNS and the brain. By any of these vias, the intestinal microbiota seems to be a fundamental modulator of this axis (F\u0026uuml;lling et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the specific mechanisms of these bidirectional communication routes between the intestinal microbiota and the brain are still unraveled (Cryan and Mazmanian, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the VN represents perhaps the most direct and fastest neural communication link between the gut microbiota and the brain (Han et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are preclinical and clinical evidences on the role of the VN in inflammatory processes that could contribute to the development of certain neurodegenerative diseases as Parkinson\u0026rsquo;s disease (Greene, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and mental disorders affecting emotion and cognition, such as depression (Inserra et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or autism spectrum disorder (Sgritta et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe VN is the longest cranial nerve with the widest innervation of body organs and it is part of the parasympathetic branch of the autonomic nervous system. It contains nearly 80% of afferent fibers, devoted to transmitting sensory information to the CNS from body organs while the remaining 20% efferent fibers carry movement information to regulate the function of different viscera including the gut (Bonaz et al., 2019).\u003c/p\u003e \u003cp\u003eThe afferent endings of the VN are located in the muscular layer and in the mucosa of the intestine, some of which synapse with neurons of the enteric nervous system, which amplify the range of signals that can be transmitted by the VN (Han et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). VN afferents have an electrophysiological response to the presence of cytokines, nutrients, peptides and hormones in the epithelial layer of the intestine. In addition, some bacteria can produce neurotransmitters such as serotonin which, when absorbed, can act directly on the VN endings or, indirectly, through activation of enteric neurons (Kaelberer et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, either mechanically or chemically, the gut microbiota is involved in VN signaling.\u003c/p\u003e \u003cp\u003eAll this raises the question of whether imbalance in the gut microbiota, which affects brain and behavior, as evidenced by the supplementation of bacterial species or transferring gut microbiota of patients with depression or Alzheimer\u0026rsquo;s Disease to germ-free animals (Allen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Grabucker et al., 2023), induces neurobiological changes either by modifying vagus nerve signaling, by slowly altering the properties of the signal or both. Changes in excitability, remodeling of synapses or sensitivity can permanently affect VN signaling and change its tone, i.e., modify the strength of vagal activation in its dynamic interaction with the parasympathetic nervous system and thus modify the properties of the VN and the neural networks on which it influences (F\u0026uuml;lling et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the vagus nerve stimulation (VNS) has demonstrated therapeutic benefits in diseases such as epilepsy and depression (Broncel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The modulation of the VN, through stimulation and modification of the signal it transmits, may be the mechanism used by the gut microbiota to activate VN afferents and reach the brain. Several studies on the neurobiology that seems to be involved in the response to VNS have revealed the existence of a neural circuit that can be considered as the afferent brain network of the VN (Hachem et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), i.e., the network formed by the subcortical and cortical brain areas to which the VN transmits its signal.\u003c/p\u003e \u003cp\u003eThe application of fMRI and PET techniques in human clinical studies have revealed the structural networks linked to the VN and the relationships among different elements of this vagal afferent network. The visceral afferent fibers contained in the VN join to the nucleus tractus solitarious (NTS) (Yakunina et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The neural pathway connecting the NTS with the locus coeruleus (LC) and the dorsal raphe nucleus (DRN), all brainstem structures, opens the way for the VN to modulate the activity of areas such as the thalamus, the limbic system and the cerebral cortex (Yakunina et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the scientific evidence that gut microbiota can modify the VN signal and this, in turn, can be reflected in brain activity and behavior, the present exploratory study aims to determine whether different microbiota composition produces different spontaneous brain activity in healthy older participants, and whether that brain activity might be related to neurodegenerative diseases and cognitive or emotional disorders before they become apparent. Positive findings would facilitate and promote further research on preventing, delaying or attenuating the onset of these disorders through simple nutritional interventions that modulate the gut microbiota.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage (standard deviation)/Percentage (#) values of the sociodemographic variables of the participants in the three microbiota-based clusters and the statistics of the main effect. BMI: Body mass index; MMSE: Minimental State Examination; DASS: Depression Anxiety Stress Scales; COS: Oviedo Sleep Quality Questionnaire.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCluster A\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster B\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster C\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.35 (6.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.00 (5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.50 (6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;.252, p\u0026thinsp;=\u0026thinsp;.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.85% (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.18% (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.67% (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e(2)\u0026thinsp;=\u0026thinsp;1.118, p\u0026thinsp;=\u0026thinsp;.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.08 (5.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.55 (3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.90 (4.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;.181, p\u0026thinsp;=\u0026thinsp;.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.08 (1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.18 (5.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.33 (1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;.928, p\u0026thinsp;=\u0026thinsp;.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASS Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27 (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.27 (3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e.50 (.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;1.555, p\u0026thinsp;=\u0026thinsp;.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASS Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64 (1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e.50 (.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;1.267, p\u0026thinsp;=\u0026thinsp;.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASS Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.92 (2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.55 (3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00 (1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;2.520, p\u0026thinsp;=\u0026thinsp;.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASS Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.35 (4.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.45 (7.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00 (2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;2.633, p\u0026thinsp;=\u0026thinsp;.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOS Subjective Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.46 (1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.32 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.67 (1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;.128, p\u0026thinsp;=\u0026thinsp;.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOS Insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.54 (5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.36 (6.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.33 (6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;.620, p\u0026thinsp;=\u0026thinsp;.542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOS Hypersomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.31 (1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.09 (2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.83 (1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;.201, p\u0026thinsp;=\u0026thinsp;.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOS Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.31 (5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.77 (6.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.83 (6.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF(2,51)\u0026thinsp;=\u0026thinsp;.598, p\u0026thinsp;=\u0026thinsp;.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eMicrobiota-based clusters\u003c/h2\u003e\n \u003cp\u003eThree clusters were identified by the X-Means clustering algorithm (A, B, and C) based on the microbiota relative population (%) at the genus level, composed of 26, 22 and 6 participants, respectively. Table 1 shows the average (standard deviation) values of the demographic and depression-related variables, together with the statistics of the differences between them. In this sense, the clusters did not present any significant difference in age, sex, body mass index, depression- and anxiety-related, and scores between them.\u003c/p\u003e\n \u003cp\u003eTo test the influence of the X-Means criterion of the optimum number of clusters on the resultant distribution, the algorithm was also executed with fixed numbers of clusters of 2 and 4. In the former case, clusters A and B were grouped as one while cluster C remained the same. In the latter case, cluster A was split into two subclusters and clusters B and C remained the same. Therefore, the inner distribution was kept regardless the number of clusters.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMicrobiota differences between microbiota-based clusters\u003c/h3\u003e\n\u003cp\u003eFigure 1a and Table S1 show the bacteria genera that presented significant statistical differences between clusters. According to Table S2, cluster B had a higher proportion of Bacteroides than clusters A and C. Besides, the proportion of Prevotella 9, Mycobacterium, Holdemanella, Coriobacteriaceae UCG-003, Libanicoccus, Lactiplantibacilus, Luenostoc and Stenotrophomonas is higher in cluster C than in the other two clusters. Cluster B contained a higher proportion of Barnesiella and Ruminococcus (gnavus group) than cluster A, whereas cluster A presents a higher proportion of Marvinbryantia and other genera than cluster B.\u003c/p\u003e\n\u003cp\u003eSummarizing (Fig. 1b), the microbiota of cluster C is mainly composed of Prevotella 9 and then Bacteroides in an inverse relation. The microbiota of cluster A is mainly composed of Bacteroides and then Prevotella 9. Despite the microbiota of cluster B is mainly composed of Bacteroides too (significantly higher than in the other two clusters), the second most abundant bacteria genus in this cluster is not Prevotella, like in cluster A, but Barnesiella. Moreover, the presence of Mycobacterium, Leuconostoc and Stenotrophomonas was evident in cluster C, in contrast to clusters A and B. However, cluster A and B evidenced the presence of Ruminococcus (gnavus group), in contrast to cluster C.\u003c/p\u003e\n\u003cp\u003eAccording to the three enterotypes defined by (Arumugam et al., 2011; Costea et al., 2018), the gut microbial composition of the clusters A and B found in the current study fits to the enterotype 1, since the genus Bacteroides is the most abundant and cluster C meets the criteria defining enterotype 2, where Prevotella is the most abundant and is inversely correlated with Bacteroides.\u003c/p\u003e\n\u003ch3\u003eSpontaneous cortical activity differences between microbiota-based clusters\u003c/h3\u003e\n\u003cp\u003eTables S2 and S3 show the cortical areas of the Brainnetome atlas that presented a statistically significant main effect of cluster in eyes closed and eyes open conditions, respectively.\u003c/p\u003e\n\u003cp\u003eThe spontaneous brain activity between clusters A and B (Fig. 2) only differs significantly in the right inferior frontal sulcus, Brodmann areas (BAs) 9 and 46, in the low alpha band (B more active than A), in the eyes open conditions.\u003c/p\u003e\n\u003cp\u003eThe differences of brain activity between cluster C, and clusters A and B were very similar (Fig. 3 and Fig. 4). Cluster C showed a higher activity than clusters A and B in the posterior cingulate cortex and the precuneus, bilaterally, and the left fusiform gyrus in the theta band during the eyes closed condition. During the eyes open condition, only the left posterior cingulate area and the left precuneus presented more activation in cluster C than in clusters A and B.\u003c/p\u003e\n\u003cp\u003eThe particular difference between the brain activation of clusters C and B (Fig. 4) is the higher activity of cluster B in the right superior primary motor cortex (M1) in the beta band, during the eyes open condition.\u003c/p\u003e\n\u003ch3\u003eCognitive functions associated to the areas with activity differences between clusters\u003c/h3\u003e\n\u003cp\u003eFigure 5 shows the activation likelihood estimation (ALE, Eickhoff et al., 2009) of the areas that significantly differed between clusters A and B (Fig.\u0026nbsp;5a), A and C and B and C (Fig.\u0026nbsp;5b), respectively during cognitive tasks according to brainmap.org.\u003c/p\u003e\n\u003cp\u003eThe only area significantly different in spontaneous activity between clusters A and B (Fig.\u0026nbsp;5b) is mainly activated during pain perception and working memory usage.\u003c/p\u003e\n\u003cp\u003eWith respect to the areas with different activity between clusters A-B and C (Fig.\u0026nbsp;5b), they are also mostly related to episodic memory, language and social cognition. However, other areas are also related to action perception and processing, cognition and emotion processing.\u003c/p\u003e\n\u003cp\u003eThis analysis reveals differences in memory, language, emotion processing and action skills partly due to microbiota composition.\u003c/p\u003e\n\u003ch3\u003eLinear modelling of brain ROIs activity from microbiota\u003c/h3\u003e\n\u003cp\u003eLinear models of the sum of ROIs activity in the theta band eyes closed (Fig. 3b and Fig. 4b), theta band eyes open (Fig. 3a, and Fig. 4a), alpha band eyes open (Fig. 2), and beta band eyes open (Fig. 4a, bottom left) were constructed. The only significant model found was the one for the sum of the source density in the theta band eyes closed, adjusted R\u003csup\u003e2\u003c/sup\u003e = .846, F(13) = 16.945, p \u0026lt; .0005. Figure 6 shows the scatterplot (\u003cstrong\u003ea\u003c/strong\u003e) of the model fitting and the relative contribution of the predictors to the model (\u003cstrong\u003eb\u003c/strong\u003e). All the predictors contributed significantly to the model (p \u0026lt; .05).\u003c/p\u003e\n\u003cp\u003eThe linear model indicates that the source density in the theta band during eyes closed condition can be estimated from some bacterial genera abundance. The most contributing genus to the model was Prevotella 9, which in turn presented significant differences between clusters A and C, and B and C. This result points to the relative abundance of Prevotella 9 as one of the sources of the difference in brain activity in the identified ROIs between clusters.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this article, a study on healthy subjects is presented in which the result of the analysis of their gut microbiota showed three groups with statistically significant differences in composition and abundance. The analysis of their spontaneous electroencephalographical activity also showed significant differences between subjects in the three groups. Based on the scientific evidence that gut microbiota can modify the VN signal and this, in turn, can be reflected in brain activity and behavior, the hypothesis underlying the findings of this study on the observed differences in brain activity is that the different groups of gut microbiota modulated differently the VN signal and, through it, they were responsible for the specific brain activity found in each group.\u003c/p\u003e \u003cp\u003eAs said before, the VN can transmit signals between the brain and peripheral organs in a bidirectional way. One of the key mechanisms to encode sensory information in the gut are by primary vagal afferent neurons, which are mechanical and chemical sensitive to gut stimuli or condition (Mayer, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accordingly, these terminals can detect and reply to gut muscular tone, inflammation or bacterial composition (Cryan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) by expressing different classes of receptors in an adaptive way depending on the homeostatic state of the host (Mayer, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). From the anatomical point of view, VN afferent fibers from the gut contact bilaterally with the nucleus tractus solitarius (NTS) (Broncel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The caudal nucleus of the NTS projects the sensory information contained in the electrical signal of the VN to the brain and receives information from different brain areas. Thus, NTS can be seen as an integrating central of these two organs (Holt \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are multiple evidences of the structural and functional connections from NTS to other close brainstem structures and distant cortical regions (Forstenpointner et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From a MRI-based research of NTS, the authors established a conceptual map of connectivity among the NTS and the brainstem, the subcortex, the cortex and the cerebellum. Regarding cortical regions, the NTS connects with the insular cortex, medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), mid-cingulate cortex (MCC), and primary somatosensory cortex (S1).\u003c/p\u003e \u003cp\u003eOther works about the role of the NTS are mainly based on the study of the VN stimulation (VNS) effects. A model of plausible vagal pathways implicated in the brain activity by VNS is proposed in (Briand et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), founded on several different noninvasive transcutaneous VNS (tVNS) fMRI studies on healthy people. According to this model, once VNS activates the structures of the lower brainstem as NTS, this one transmits activation to upper brainstem areas as the LC and DRN. The activation of LC produces norepinephrine that modulates large-scale brain networks involved in cognitive functions like memory, attention, emotion or stress reaction. At the cortical level, this pathway may reconfigure the activity of the insula, the dorsolateral and dorsomedial prefrontal cortices and the sensory cortex. Furthermore, the activation of DRN produces serotonin, which selectively affects to brain regions of the limbic system as the ACC, the posterior cingulate cortex (PCC) and the dorsomedial prefrontal cortex (dmPFC). Most of these areas have been involved in our study.\u003c/p\u003e \u003cp\u003eIn line with this model, a review of several studies contained in (Broncel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also describes how the vagal stimulation applied to, either healthy and neurological impaired people, impacts in the LC activity increasing the release of neurotransmitters as the norepinephrine, which is involved in most of the structures related to memory. Besides, the norepinephrine, projected by the LC to the cerebral cortex, stimulates the receptors of the DRN, which projects serotonin to the cerebral cortex. Both neurotransmitters have neuroprotective effects for the brain and modulate neurogenesis (Cheyuo et al., 2011).\u003c/p\u003e \u003cp\u003eAnother anatomical model dealing with the modulation of the brain activity and functional connectivity by tVNS is proposed in (Liu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The model collects the evidences on neuroimaging biomarkers obtained from different studies with healthy participants. The stimulation of VN changes the activation in the insula, precentral gyrus, PCC, ACC, prefrontal and frontal cortices, S1 and parahippocampal gyrus, as it happens in our case. On the other hand, in people with major depressive disorder (MDD), the treatment with VNS produces changes in functional connectivity between the posterior default mode network (DMN) (precuneus and PCC) and the emotional and reward structures (insula and parahippocampus), between the posterior and anterior (ACC, nucleus accumbens and ventromedial prefrontal cortex) DMN, between the anterior DMN and the dorsomedial and dorsolateral prefrontal cortices, and between dorsomedial and dorsolateral prefrontal cortices and emotional and reward structures. This work also highlights the anti-inflammatory role of VNS in ameliorating depressive symptoms in some subtypes of MDD since the VN can be considered as an interface among neurotransmitters, neuroendocrine system, neuroinflammation and gut-related immunity.\u003c/p\u003e \u003cp\u003eVNS has also been studied in healthy people to find its influence on cognitive functions by analysing the neural pathways engaged in VNS action. Most of these studies focuses on the prefrontal cortex and hippocampus and on the role of neurotransmitters as norepinephrine, serotonin and GABA (Klaming et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in cognition and emotion. In (Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), synchronized fMRI and tVNS were acquired to analyse the effects of VNS on brain activity and cognitive performance. This work showed that VNS results on a better performance on memory and language skills and produces correlated significant spontaneous brain activity changes on the calcarine gyrus, fusiform gyrus, lingual gyrus, and parahippocampal gyrus. In addition, it has been widely evidenced that the left fusiform gyrus is involved in the emotion processing of faces (Monroe et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jung et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) Since the transcutaneous stimulation of the vagus nerve enhances recognition of emotions in faces (Sellaro et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the differences in source density in that area found in our study are plausibly explained by vagus nerve path, which is signaled in the gut. In older healthy people, tVNS improves associative memory performance based on the hypothesis that tVNS modulates the memory using similar mechanisms to how emotion affects memory: emotional arousal increases the level of adrenalin that activates afferent VN fibers, and these activate the LC, which releases noradrenalin to the hippocampus and the amygdala that are brain areas involved in memory formation and consolidation (Jacobs et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Other neuroimaging studies revisited in (Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) point to the precuneus as a cortical brain area involved in visuospatial imagery, contextual memory and information processing, among others, and to the parahippocampal gyrus as the main cortical input to the hippocampus. Both regions are significantly activated when tVNS is applied, as can be seen in our study. The concomitant increase of norepinephrine concentration in prefrontal cortex following tVNS has also demonstrated its effects on inhibitory control processes modulated by working memory load (Beste et al., 2016). In the same way, tVNS raises GABA levels and both neurotransmitters enhance attention and executive functions (Klaming et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCaution is required when interpreting the results of our study. The assumption that different gut microbiota composition leads to signal specific brain cortical areas in a distinct way mediated by the vagus nerve does not necessarily imply that gut microbiota is the sole cause of the brain activity differences. It is necessary to keep on making studies that include more factors that can have impact on gut microbiota differences as pointed out in (Cryan and Mazmanian, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough there were not found significant differences in the MMSE and DASS21 tests administered among people included in the three microbiota clusters, since they are all healthy participants, the differences in cortical activity in the areas found for the different microbiota clusters suggest that the microbiota may modulate some cognitive abilities likely to be impaired with age in the absence of other neurological and neuropsychiatric pathologies.\u003c/p\u003e \u003cp\u003eThe main areas that presented differences in spontaneous activity between microbiota-based clusters in our study, the precuneus and the posterior cingulate cortex, are considered as a central hub part of the posterior default mode network (DMN) (Utevsky, Smith and Huettel, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the absence of pathology, these areas have been traditionally related to functions such as visuo-spatial imagery, episodic memory and self-processing (Cavanna and Trimble, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), although the participation in working memory, reward and fear processing has been also recently evidenced (Dadario and Sughrue, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, given their widespread functional and structural connections and their high relative metabolic rate (Leech and Sharp, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), any disorder on them might lead to a variety of cognitive changes (Kumral, Bayam and \u0026Ouml;zdemir, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a matter of fact, the precuneus and PCC have been pointed as key vulnerable areas for several neuropathologies and psychiatric disorders (Dadario and Sughrue, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). With regard to our study, a psychiatric disorder has been shown to concur with an increase in resting metabolism in the precuneus and PCC (as cluster C in our study): major depressive disorder (MDD) (Leech and Sharp, 2014). Since participants ultimately included in the analysis did not present symptoms of depression according to the DASS test, and all cluster were homogeneous in this sense, MDD can be discarded as the cause of the differences in spontaneous activity found between clusters in the precuneus and PCC. Besides, the area that presented difference in spontaneous activity between clusters A and B, the right inferior frontal sulcus (IFS), BAs 9 and 46, is mainly implicated in working memory and differences in cortical thickness due to age were evidenced to correlate with working memory performance (Eich, Lao and Anderson, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given also that the IFS is an area differentially affected by ageing (Feng et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the difference in microbiota between clusters A and B points to a difference in healthy brain ageing (despite no difference in chronological age). Again, this needs further research.\u003c/p\u003e \u003cp\u003eSummarizing, this paper presents the first study to assess the relation between gut microbiota and spontaneous brain activity measured by resting-state EEG in healthy senior people. We have reported two main findings: 1) gut microbiota composition could impact on brain activity, which has been demonstrated by two procedures, based on a cluster analysis and on a predictive model; and 2) the brain activity could influence the working memory aging process.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant recruitment and sample collection\u003c/h2\u003e \u003cp\u003eThe recruitment was performed through the GENYAL Clinical Trials Platform at IMDEA Food Institute. This study was approved by the institutional Research Ethics Committee (protocol ID: IMD PI-055, IMDEA Food Foundation) and performed in accordance with the principles of research involving human subjects stated in the Declaration of Helsinki (1964) and Good Clinical Practice (CGP). All participants were clearly informed about the study methodology and provided written informed consent.\u003c/p\u003e \u003cp\u003eAnthropometric measurements, peripheral blood samples and fecal samples were collected from a total of fifty-four healthy participants over 55 years of age (Male: 21, Female: 33). Sampling and data acquisition were performed between October and November 2022.\u003c/p\u003e \u003cp\u003eInclusion criteria included: Age\u0026thinsp;\u0026ge;\u0026thinsp;55 years; BMI between 27\u0026ndash;35 kg/m2; able to understand the informed consent; willing to comply with the study protocol. Exclusion criteria included: decreased cognitive function, pregnancy or breastfeeding, severe chronic health conditions (heart, liver, etc.), BMI\u0026thinsp;\u0026gt;\u0026thinsp;35 kg/m2, or pharmacological treatment such as anxiolytics, antidepressants, medication for sleep disorders, or any type of psychotropic medication.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric Measurements\u003c/h2\u003e \u003cp\u003eAnthropometric measurements were collected following standardized methodology. Body Composition Monitor analyzer (BF511- OMRON HEALTHCARE UK, LT, Kyoto, Japan) was used to determine weight, % body fat, % skeletal muscle and visceral fat, as well as basal metabolic rate. Height was measured with a stadiometer (Leicester-Biological Medical Technology SL, Barcelona). Waist and hip circumference (cm) were measured using Seca 201 (Quirumed, Valencia, Spain). Blood pressure and pulse were monitored with the Model M3 blood pressure monitor from OMRON HEALTHCARE UK, LT, Kyoto, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBehavioral measures: sleep habits (OSQ questionnaire), emotional state (DASS-21 questionnaire) and mental state (MMSE questionnaire)\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eOSQ\u003c/h2\u003e \u003cp\u003eSleep habits were assessed via the Oviedo Sleep Questionnaire (OSQ) (Bobes et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The OSQ is a validated interview that support diagnosis of insomnia and hypersomnia during the previous month based on the DSM-IV and ED-10 criteria. The questionnaire is made up of a total of 15 items including Subjective satisfaction with sleep, Insomnia (difficulty with falling or remaining asleep, early awakenings or restorative sleep, among others), hypersomnia (daytime sleepiness and its effects on daily tasks).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDASS-21\u003c/h2\u003e \u003cp\u003eThe psychological state of the participants was measured using the short version of the Depression, Anxiety and Stress Scale (DASS-21) (Antony et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). designed to assess the emotional states of depression, anxiety, and stress. The DASS-21 consists of 21 items, divided into three subscales of 7 Likert items. A score for each scale is calculated by summing all the related items and these scores are later added to obtain a general score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMMSE\u003c/h2\u003e \u003cp\u003eThe Mini-Mental State Examination (MMSE) (Spanish version) is a widely utilized cognitive screening tool designed to assess cognitive impairment and dementia (Folstein et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Lobo et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). The MMSE evaluate five cognitive domains: orientation, registration, attention and calculation, recall, and language. The total score has a maximum punctuation of 35, with lower scores indicating greater cognitive impairment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFecal samples processing and 16S sequencing\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eSample Collection and DNA Extraction\u003c/h2\u003e \u003cp\u003eFecal samples were sent to Novogene for 16S sequencing of the bacterial V3V4 region: DNA was extracted by using Magnetic Soil and Stool DNA Kit (TianGen, China, Catalog #: DP712), following the manufacturer's protocol to ensure high-quality and high-yield DNA suitable for downstream applications.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePCR Amplification of 16S rRNA Gene\u003c/h2\u003e \u003cp\u003eThe 16S rRNA gene was amplified using a set of universal primers targeting the V3-V4 regions of the 16S rRNA gene. The primers used were (5'- CCTAYGGGRBGCASCAG-3') and (5'- GGACTACNNGGGTATCTAAT-3'), which are known for their broad coverage of bacterial taxa while minimizing amplification of eukaryotic rRNA genes. The PCR conditions were optimized to ensure specific amplification, with an initial denaturation at 98\u0026deg;C for 1 minute, followed by 30 cycles of denaturation at 98\u0026deg;C for 10 seconds, annealing at 50\u0026deg;C for 30 seconds, and extension at 72\u0026deg;C for 30 seconds, with a final extension at 72\u0026deg;C for 5 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eHigh-Throughput Sequencing\u003c/h2\u003e \u003cp\u003eThe amplified 16S rRNA gene fragments were performed by using specific primers connecting with barcodes. The PCR products of proper size were selected through 2% agarose gel electrophoresis. The same amount of PCR products from each sample was pooled, end-repaired, A-tailed, and further ligated with Illumina adapters. Libraries were sequenced on a paired-end Illumina platform to generate 250bp paired-end raw reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eData Processing and Analysis\u003c/h2\u003e \u003cp\u003eRaw sequencing data were processed using the DADA2 pipeline, which includes quality filtering, dereplication, chimera removal, and sequence variant inference. This method allows for the recovery of full-length 16S rRNA gene sequences with single-nucleotide resolution and a near-zero error rate, ensuring high accuracy in microbial community profiling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eOperational Taxonomic Unit (OTU) Clustering\u003c/h2\u003e \u003cp\u003eSequences were clustered into Operational Taxonomic Units (OTUs) using a de novo clustering approach, which has been shown to outperform reference-based methods in terms of stability and quality of OTU assignments. The clustering was performed using the VSEARCH algorithm, which is a viable open-source alternative to USEARCH.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic Assignment\u003c/h2\u003e \u003cp\u003eTaxonomic classification of the OTUs was performed using the SILVA database, which provides comprehensive and up-to-date taxonomic information for 16S rRNA gene sequences. The classification was done at various taxonomic levels, including phylum, class, order, family, genus, and species.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eEEG Data Acquisition\u003c/h2\u003e \u003cp\u003eThe participants were comfortably seated with their forearms resting in their thighs, one meter away from a white wall. They were asked to relax and breathe deeply with their mouth naturally open during three-minute periods, first with eyes closed and then with eyes open staring at the wall, with a one-minute resting interval between the two conditions. The EEG signal was acquired from 32 wet active ActiCAP electrodes (Brain Products GmbH, Gilching, Germany) placed on the scalp in the positions Fp1, Fp2, AF3, AF4, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, PO3, PO4, O1, Oz, O2 according to the 10\u0026ndash;20 system. Reference was set to the Fz electrode and ground to the Fpz electrode. The signal was digitized and recorded by a actiCHamp amplifier (Brain Products GmbH, Gilching, Germany) at 256Hz. The impedance of all electrodes was kept under 10 kΩ.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eEEG Processing\u003c/h2\u003e \u003cp\u003eThe EEG signal was preprocessed offline by a custom script in Matlab R2019b (The MathWorks Inc., Natick, MA, USA) using the EEGLAB (Delorme \u0026amp; Makeig, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) functions. The signal was first cleaned by using Artifact Subspace Reconstruction (ASR) (Kothe \u0026amp; Makeig, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), rejecting burst intervals above a threshold of 20 standard deviations (all the other parameters set to default). Then, the signal was bandpass filtered between 3Hz and 31Hz. After that, eye- and muscle- related artifacts were removed by IClabel (Pion-Tonachi et al., 2019) with thresholds above 70% of probability. Next, bad channels were interpolated using the default parameters of the PREP pipeline (Bigdely-Shamlo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Finally, the signal was rereferenced to the average value among all channels.\u003c/p\u003e \u003cp\u003eSource density in six frequency bands (theta: 4Hz-7Hz; low alpha: 7Hz-10Hz; high alpha: 10Hz-13Hz; low beta: 13Hz-18Hz; mid beta: 18.5Hz-21Hz; high beta: 21Hz-30Hz) was calculated from the preprocessed signal by the eLORETA algorithm (Pascual-Marqui et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) implemented in the LORETA-KEY software v20221229 (KEY Institute for Brain-Mind Research, Zurich, Switzerland). Source density was averaged for each of the 210 cortical areas defined by the Brainnetome atlas (Fan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eData and statistical analysis\u003c/h2\u003e \u003cp\u003eParticipants were clustered from their microbiota population (at genus level) by the X-Means algorithm (Pelleg \u0026amp; Moore, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), which automatically determines the optimum number of clusters (maximum intra-cluster, minimum inter-cluster similarity) based on the Bayesian Information Criterion (BIC).\u003c/p\u003e \u003cp\u003eDifferences in socio-demographic variables and microbiota population were tested by MANOVA tests, after confirmation of normality with the Shapiro-Wilk test and Q-Q plots. Post-hoc pairwise comparisons were performed with Tukey\u0026rsquo;s method after confirmation of significant main effect of cluster. Differences in categorical variables were tested by the Chi-squared test. Significance was considered with p\u0026thinsp;\u0026lt;\u0026thinsp;.05. Effect size was reported as partial eta squared.\u003c/p\u003e \u003cp\u003eDifferences in EEG source densities between clusters were tested by Kruskal-Wallis test, after confirmation of non-normality by the Shapiro-Wilk test and Q-Q plots. Significance was considered with p\u0026thinsp;\u0026lt;\u0026thinsp;.01 in this case. Post-hoc pairwise comparisons were performed with Bonferroni\u0026rsquo;s correction after confirmation of significant main effect of cluster. All the above analyses and tests were performed with SPSS v29.0.2.0 (Armonk, NY: IBM Corp.).\u003c/p\u003e \u003cp\u003eFinally, linear regression models were applied to predict the source density sum of the ROIs that presented significant differences between clusters. For that purpose, the automatic linear modelling procedure of the SPPS v29.0.2.0 (Armonk, NY: IBM Corp.) was applied, setting the number of predictors to the number of bacteria genera that presented significant differences between clusters, and Akaike Information Criterion (AIC) as model fit index. Models with p\u0026thinsp;\u0026lt;\u0026thinsp;.05 (ANOVA) were considered statistically significant. Importance of each predictor was calculated as the residual sum of squares with the predictor removed from the model, normalized so that all the importance values sum up to 1.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants PID2023-150271NB-C21 and PID2023-150271NB-C22 funded by MICIU/AEI/ 10.13039/501100011033 (Spanish Ministry of Science, Innovation and University, Spanish State Research Agency). Other grants involved were PID2022-138295OB-I00 funded by MICIU/AEI/ 10.13039/501100011033, RTC2019-007294-1 funded by MICIU/AEI/ 10.13039/501100011033 and “FEDER/UE”, and PIE202350E170 funded by the Spanish National Research Council. Also supported by Regional Government of Community of Madrid (NutriSION-CM Y2020/BIO-6350).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors thank the volunteers for participation and commitment in the study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.I.S.: conceptualization, methodology, software, verification, formal analysis, investigation, data curation, writing – original draft, visualization; S.C.-G.: conceptualization, methodology, investigation, resources, data curation, writing -original draft, visualization; C.M.F.: conceptualization, methodology, investigation, resources, data curation, writing -original draft; L.P.F.: conceptualization, methodology, investigation, resources; A.S.: software, investigation, writing- review \u0026amp; editing; I.E.-S.: conceptualization, methodology, investigation, resources; C.M.: conceptualization, methodology, investigation, resources; M.-J.L.: conceptualization, methodology, funding acquisition; M.C.: validation, writing – review \u0026amp; editing, visualization; R.R.-R.: methodology, project administration, funding acquisition, supervision; A.R.d.M.: conceptualization, methodology, supervision; M.D.d.C.: conceptualization, methodology, verification, formal analysis, investigation, resources, writing – original draft, supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the EEG processing scripts are openly available at GitHub repository (https://github.com/COMODIN-group/ProtocoloSENIOR).The packages are completely open for use, see documentations: EEGLAB (https://eeglab.org/).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen, A. 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In the gut-to-brain way, the gut microbiota has been shown to be the main factor. The physiological and biochemical mechanisms of this communication have been described in detail, and the gut microbiota has been shown to be altered in many neurological and psychiatric conditions. However, it is unknown how the gut microbiota influences the brain activity, especially in the healthy condition. By clustering healthy older people by their corresponding microbiome and comparing their spontaneous cortical activity, we demonstrate that different unaltered microbiota profiles are associated with different spontaneous activity in medial posterior cortical areas. These areas are associated to memory, language and emotion processing abilities. Therefore, we provide evidence that normal gut microbiota profiles modulate spontaneous cortical activity related to cognitive functions that typically decline with age. 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