Electrophysiological sex-dimorphism as early risk markers of alcohol use in adolescence

Electrophysiological sex-dimorphism as early risk markers of alcohol use in adolescence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Electrophysiological sex-dimorphism as early risk markers of alcohol use in adolescence Alberto del Cerro-León, Marcos Uceta, Danylyna Shpakivska-Bilan, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6057213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Heavy drinking during adolescence is associated to alterations in the electrophysiology of the brain. However, some of these alterations are observable at pre-consumption stages. To identify the brain activity profiles associated with alcohol consumption and to address its causes, we examined the power spectra and functional excitation-inhibition ratio in a cohort of 55 adolescents within a two-stage study separated by a two-year follow-up. Our results indicate that power-spectra in beta-band showed a positive correlation with alcohol use during both phases of the study (1st: rho = 0.34, p = 0.042; 2nd: rho = 0.37; p = 0.02) and a negative correlation with excitation-inhibition ratio (1st: p < 0.05; rho=-0.30; 2nd: p < 0.01; rho = -0.43). Finally, biological sex showed strong moderation effect, were females drive the predictive relationship (p < 0.001; rho = 0.66; β=-0,61). Taken together, these results indicate that electrophysiological differences prior to consumption are predictive of future alcohol use in females and associated with activity profiles prone to inhibition. Health sciences/Biomarkers/Predictive markers Biological sciences/Physiology/Neurophysiology Figures Figure 1 Figure 2 Figure 3 Significance statement Alcohol consumption during adolescence is associated with alterations in brain activity. To differentiate the effects of alcohol from predisposing traits, a longitudinal study was carried out which demonstrated the existence of anomalies in the frequency spectrum in stages prior to consumption and which were maintained 2 years later. Furthermore, it was found that these differences were associated with changes in inhibitory tone that occur during adolescence. Finally, moderation analyses revealed that the associations between brain activity and alcohol consumption were influenced by sex, being especially relevant in females. 1. Introduction The last 20 years of animal, neuroimaging, and behavioural research have demonstrated that adolescence is a critical period of brain development, characterized by changes in both brain structure and function 1 and directly involved in higher-order and complex cognition 2 , 3 . During this period, adolescents are particularly sensitive to rewarding effects, which may lead to risky behaviours such as substances misuse that impact on social, psychological, and neurobiological development 4 . In this regard, researchers has demonstrated that alcohol use is linked to alterations in electrophysiological activity such as increased theta and beta band power in frontal regions 5 and posterior occipito-temporal cortices 6 , 7 . Similarly, functional connectivity studies have also highlighted disrupted resting-state networks associated to alcohol HD 8 , 9 . However, growing evidence underscores the need for prospective longitudinal studies to differentiate neurobiological factors predisposing individuals to HD from those resulting from consumption 10 , 11 . However, no studies have yet characterized spontaneous power spectra in relation to alcohol use onset during adolescence development. The onset of puberty involves several neurobiological changes that leave a distinctive fingerprint on spontaneous electrophysiological activity 12 , 13 . During normative development, synaptic pruning and interneuron maturation in the brain 14 – 16 lead to faster power spectral density oscillations, particularly in the alpha, beta, and gamma bands 17 – 20 . However, internal factors such as sex hormones have a critical role causing significant sex-dimorphisms in the neurological maturation 21 , 22 . In males, cortical maturation influenced by testosterone occurs gradually and prolonged in time. In contrast, female’s puberty begins earlier and progress faster due to earlier increases in estradiol 23 – 25 , with a more pronounced increase in beta-band activity 18 , 19 . In turn. recent studies, such as McSweeney et al. (2021), have also demonstrated sex-dependent maturation in aperiodic brain activity, an index of the excitatory/inhibitory (E/I) balance. This differential brain maturation due to sex-related factors further influences the reward system, potentially shaping the development of substance consumption habits. In males, testosterone targets the nucleus accumbens, promoting risk-taking behaviours 27 , 28 , whereas in females, estradiol amplifies dopaminergic release, leading to heightened vulnerability to the rewarding effects of substances 28 – 30 . This prospective longitudinal study aimed to distinguish between neurobiological factors that predispose individuals to heavy drinking and those resulting from alcohol consumption itself. We hypothesize that some electrophysiological differences in power spectra previously reported in young adults with binge drinking (BD) are present before the onset of alcohol consumption, serving as predisposition markers. Furthermore, these differences in power spectra might emerge from alterations in the excitation-inhibition (E/I) balance and influenced by biological sex. We conducted three analyses within a longitudinal study framework. First, we used magnetoencephalography (MEG) to measure resting-state brain power spectra in a cohort of adolescents both before and after engaging in heavy drinking (HD). Next, we explored the relationship between these electrophysiological profiles and the E/I balance to better understand their underlying neural mechanisms. Finally, given known sex differences in neurodevelopment, we examined the effect of biological sex on these brain activity profiles. 2. Methods and Materials 2.1 Participants The participants were recruited from two projects funded by the Spanish Ministry of Health in 2015 and 2019. Samples were collected from different high schools across the community of Madrid, following an identical evaluation protocol in two stages separated by a two-year follow-up period. Participants were screened to ensure no history of alcohol consumption, family alcohol use and psychiatric or neurological disorders. All participants completed the Alcohol Use Disorder Identification Test (AUDIT) 31 and a semi-structured interview regarding substance use habits. In the first stage, before the onset of alcohol use, 148 adolescents participated in a Magnetoencephalography (MEG) recording of 5 minutes in resting-state with eyes closed. 121 of those participants also underwent magnetic resonance imaging (MRI) study. After the two-year follow-up period, 73 participants were re-evaluated. Based on AUDIT and interview information, we calculated the quantity of Standard Alcohol Units (SAUs) (1 SAUs = 10mg ethanol) consumed during regular drinking episodes, considering the number and type of beverages consumed within 2–3-hour. Tobacco and cannabis use were monitored, and 2 participants with regular use were excluded. We used self-report scales to evaluate traits of impulsivity (Barratt Impulsiveness Scale 32 (BIS-11)), sensation seeking (SSS-V 33 ), and dysexecutive behaviors (Barkley Deficits in Executive Functioning Scale 34 (BDEFS), the Behavior Rating Inventory of Executive Function 35 (BRIEF), and the Dysexecutive Questionnaire 36 (DEX)). After quality control of the MEG and MRI data, a final sample of 55 subjects (32 males and 23 females) completed the protocol. Informed consent was obtained from all participants and their parents or legal guardians in accordance with the Declaration of Helsinki and the study received ethical approval from the ethics committee of the Universidad Complutense de Madrid. 2.2 MRI recordings and volumetry The participants underwent a high-resolution 3D T1-weighted brain MRI scan at either the Santa Elena Foundation (General Electric Optima MR450w, 1.5 T) or the Clinical Hospital of Madrid (General Electric Signa HDxt, 1.5 T). The scan parameters for both machines included echo time: 4.2 ms, repetition time: 11.2 ms, inversion time: 450 ms, field of view: 100, acquisition matrix: 256 × 256, and slice thickness: 1 mm. 2.3 MEG recordings MEG data were acquired using a 306-channel (102 magnetometers and 204 planar gradiometers) whole head Elekta Neuromag system located in a magnetically shielded room at the Center for Biomedical Technology in Madrid, Spain. Brain activity was recorded during an eyes-closed resting state using an online FIR-type anti-alias filter with a frequency range of 0.1 to 330 Hz and a sampling rate of 1,000 Hz. The head shape of each participant was captured using a Fastrak digitizer (Polhemus, Colchester, Vermont) with three fiducial landmarks (nasion and left and right pre-auricular points). Four Head Position Indicator (HPI) coils were attached to the participant’s scalp to track head position. Additionally, two sets of bipolar electrodes were used to monitor eye blinks and heartbeats. 2. 4 Signal processing and source-space reconstruction To reduce environmental noise and correct for subject movements, a temporal extension of the signal space separation (tSSS) method was applied 37 . The Fieldtrip software 38 in Matlab R2020b was used to automatically detect artifacts in the MEG signal, which were then visually confirmed by an MEG expert and the artifact-free data were segmented into 4-second epochs with 2 additional seconds of real data on either side as padding. Individual MEG signals were estimated at the source level using participant's T1-weighted MRI using an homogeneous grid based on the Automated Anatomical Labeling (AAL) atlas 39 . This grid comprised 1202 positions corresponding to 78 cortical regions, which were transformed into the participant's space using a linear transformation between the MNI template and the participant's T1-weighted MRI. Additionally, the T1-weighted image was employed to create a single-shell head model based on the inner surface of the skull 40 . Finally, a linearly constrained minimum variance (LCMV) beamformer was applied as the inverse method to reconstruct the signal in the cortical sources. Analysis 1: Characterization of power-spectra profiles related to alcohol use 2.5 Power spectra calculations The power spectrum at each source was computed using the Fieldtrip toolbox 38 and a multitaper method (mtmfft) with discrete prolate spheroidal sequences (dpss) as the windowing function and 1 Hz smoothing. We analysed relative power by normalizing each frequency step by the total power across the entire spectrum in the 2–45 Hz range. This resulted in a source-reconstructed matrix with dimensions of 1202 nodes x 173 frequency steps x 55 participants. 2.6 Statistical analysis: Cluster based permutation test (CBPT) To identify power spectra patterns related to SAUs, we performed a cluster-based permutation test (CBPT) implemented in Fieldtrip 38,41 . Firstly, Spearman’s partial correlation tests between normalized power across different frequency bands (theta = 4-8Hz, alpha = 8-12Hz, low-beta = 12-20Hz, high-beta = 20-30Hz and gamma = 30-45Hz) and SAUs were calculated per node controlling for age, sex and project. Rho values at p < 0.05 where thresholded and clustered considering adjacent nodes in space-frequency. Clusters must exceed 1% of total nodes and show significance in three consecutive frequencies. Then, rho values were converted to Fisher’s Z, computing the cluster mass statistics as the sum of Z values. To control for multiple comparison, the statistical analysis was repeated 50000 times with shuffled data to create a null distribution. Once calculated, the empirical distribution allowed us to calculate the p-value corresponding to each of the original candidate clusters. Finally, clusters with CBPT p < 0.05 were considered for further analysis. Analysis 2: Correlation between power spectra and excitation-inhibition ratio ( f E/I) 2.7 Functional excitation-inhibition ratio To estimate the excitation-inhibition ratio ( f E/I) we applied the algorithm developed by Bruining et al., 2020. The segmented source-space signals were converted into continuous time-series by concatenating segments and interpolating over artifact-disconnected gaps. The signal profile for each source was defined as the cumulative sum of the demeaned amplitude envelope, segmented into 5-second windows with 80% overlap. Each window was amplitude-normalized, detrended, and its standard deviation calculated. fE/I values were estimated in 1202 sources within the low beta band (12–20 Hz) using the Pearson correlation between windowed mean amplitudes and standard deviations. Interpretation: fE/I > 1 indicates excitation dominance, fE/I 0.55 to exclude unreliable values. 2.8 Statistical analysis: Partial correlation To analyze the association between the power-spectra and levels of f E/I, Spearman’s partial correlations were calculated for each stage of the study, controlling for the effects of sex, age and project. Analysis 3: Sex effect in the characterization of power spectra profiles 2.9 Statistical analysis: Moderation Moderation effects were assessed using the Statistical Package for the Social Sciences (SPSS) 29.0.2.0 and the macro Process 43 version 4.3 (www.processmacro.org/index.html). The relative power of the cluster was used as predictor, the SAUs as dependent variable and sex as moderator. In addition, age was included as a covariate to eliminate its effect on the analysis. 3. Results 3.1 Demographics After the follow-up period, adolescents had an average alcohol consumption of 3.9 ± 2.6 SAUs with no significant difference between males and females. Regarding behavioural variables, differences were shown in sensation seeking during the first phase of the study (p-value = 0.017), where men had higher scores on the SSS-V questionnaires (19 ± 6.1) compared to their female counterparts (14.8 ± 6.3). The means and standard deviations of the whole sample and both sexes as well as the differences between groups are detailed in Table 1 . Table 1 Alcohol consumption, age and cognitive scale scores over the course of the study. Whole (55) Male (32) Female (23) p-value UBEs 3,9 (2,6) 3,9 (2,6) 3,9 (2,7) 0,91 1st Phase Age 14,5 (0,7) 14,5 (0,7) 14,4 (0,6) 0,54 SSS_V 17,2 (6,5) 19,0 (6,1) 14,8 (6,3) 0,02* BIS_11 58,2 (14,4) 56,1 (13,9) 61,0 (15) 0,22 PCA_exe 0 (25,8) 1,0 (28,1) -1,3 (22,6) 0,75 2nd Phase Age 16,4 (0,6) 16,5 (0,7) 16,3 (0,6) 0,36 SSS_V 21,62 (5,0) 22,3 (5,2) 20,8 (4,8) 0,33 BIS_11 63,0 (8,1) 62,2 (8,2) 64,1 (8,1) 0,44 PCA_exe 0 (23,5) -4,6 (21,6) 5,74 (25) 0,13 * Marks significant differences between males and females. 3.2 Experiment 1: Power-spectra relates to Alcohol use 3.2.1 Pre-consumption MEG CBPT analysis reveals a positive correlation between future alcohol consumption and spectral power between 12 to 18.50 Hz (Fig. 1 ). At lower frequencies, the cluster encompass the bilateral temporal lobes, expands with increasing frequency to occipital and frontal regions, to finally be reduced to a few frontal and occipital sources at higher frequencies. Spearman’s rho ranged between 0.23 and 0.46, with an averaged statistical parameter of p = 0.042 and rho = 0.34. 3.2.1 Post-consumption MEG Two years later, positive correlation maintained a significant association with SAUs (CBPT p-value = 0.02, rho = 0.37) between 12 to 20 Hz (Fig. 2 ) with Spearman’s rho ranged between 0.23 and 0.55. At lower frequencies, the cluster were limited to bilateral temporal regions, extended toward occipital, parietal and inferior frontal regions, and finally were reduced to occipital sources at higher frequencies. 3.3 Experiment 2: More low-beta power is associated with lower f E/I values The mean duration of the time-series was 281.55 s and surpassed 180.25 s in all cases. After correcting the f E/I values by the DFA exponents, an average of 80.81% of the sources were marked as reliable during the first phase of the study and 88.19% during the second phase, constituting a representative measure of the excitation-inhibition balance. Once calculated, the f E/I values were correlated with cluster power obtained in the first stage of the study, finding a significant negative association (p-value < 0.001; Spearman’s rho = -0.57). Moreover, this association was maintained during the second stage of the study (p-value < 0.01; Spearman’s rho = -0.42) (Fig. 3 ). In addition, f E/I values during the first phase of the study also showed a positive correlation with future consumption (p-value < 0.05; Spearman’s rho = -0.34). 3.4 Experiment 3: Sex moderates the relationship between power spectra and alcohol use Moderation analysis revealed a significant interaction between sex and relative power during the first stage of the study, where the importance of prediction was relevant only in the female group (p-value < 0.001; Spearman’s rho = 0.66). Moderation analyses during the second stage found a predictive value of alcohol on relative power without significant interaction with sex. A detailed description of the models is depicted in Table 2 . The partial correlations between relative power and consumption in the whole sample and in each of the sexes can be found in Supplementary material 1 . Table 2 Moderation analysis of sex in the prediction of SAUs β SE t p-value 1st Phase Constant 3,70 2,84 1,30 0,20 Low beta power (Z-scored) 0,66 0,18 3,66 < 0,01 Sex 0,09 0,25 0,36 0,72 Sex * Low beta power -0,61 0,25 -2,42 0,02 Age -0,26 0,20 -1,33 0,19 Conditional effects Females 0,66 0,18 Males 0,04 0,18 2nd Phase Constant 3,94 3,46 1,14 0,26 Low beta power (Z-scored) 0,52 0,18 2,85 < 0,01 Sex 0,16 0,27 0,60 0,55 Sex * Low beta power -0,26 0,26 -0,99 0,32 Age -0,25 0,21 -1,17 0,25 4. Discussion 4.1 Summary of the main findings This study aimed to characterize the electrophysiological power-spectra and excitation-inhibition balance associated with heavy drinking (HD) onset, and its impact on brain activity during adolescence. Additionally, we explored how biological sex moderate this association. Findings revealed that increased beta-band power in the frontal, temporal, and parieto-occipital cortices was related to higher alcohol consumption two years later. A similar pattern emerged after drinking onset, with higher beta-band power linked to increased consumption rates. These electrophysiological features were associated with lower excitation-inhibitory ratio and strongly moderated by sex. 4.2 Alterations in power due to heavy alcohol consumption Previous literature reported that HD consumption in university young adults cause an increase in beta band power during resting-state EEG, particularly in frontal and temporal regions 4 , 5 , 7 and decreases in low frequencies 5 , 44 . Such results were explained as a compensatory mechanism, according to which, the brain allocates a higher resources to compensate for underlying neural deficits 6 . Notably, these alterations resemble those observed in alcohol-dependent individuals, suggesting that HD may share similar neurotoxic effects 45 . However, current results suggest that electrophysiological differences in beta-band exist prior to consumption and persist throughout adolescence following initiation of alcohol use. This evidence aligns with prior findings that suggest the existence of functional connectivity profiles prone to consumption 10 , 11 . However, no associations were found between alcohol consumption and alpha or theta power at any stage. This suggests that decreased slow-band power may result from prolonged consumption. 4.3 Implications of excitation-inhibition balance in the beta band Beta rhythm is widely recognized for its broad involvement in cognitive processes and brain dynamics 46 – 48 . During resting state, it is proposed to facilitate the integration of resting-state networks and support the brain’s global efficiency 46 . On the other hand, excessive beta band has been associated with reduced cognitive flexibility, ruminative thoughts, compulsive behaviours 46 , 47 , 49 , and addiction disorders 50 . In light of recent results, it has proposed that increases in beta band reflect an excitatory-inhibitory imbalance potentially linked to alterations in the GABAergic and glutamatergic neurotransmitter systems 5 , 7 , 51 . In accordance with these hypotheses, animal and human studies have shown that continued alcohol use causes a decrease in GABAa receptors and an excitatory upregulation 52 , 53 . Conversely, in our results, increases in beta power were associated with more inhibition-prone ratio. In this regard, during adolescent neural development changes in electrophysiological activity are a common phenomenon 54 due to cortical circuitry transformations 55 , 56 . This process is influenced by facilitating factors, such as synaptic pruning and the maturation of PV + interneurons, which facilitated local evoked activity and enhance neuronal plasticity 14 – 16 Meanwhile, myelin band development stabilizes neural networks and increases transmission speed between cortical regions 14 , 55 , 57 . Taken together, these developmental changes promote shifts in electrophysiological activity towards faster-frequency bands, particularly in posterior regions and the precentral gyrus 18 , 20 , 54 . Thus, differences prior to alcohol consumption may arise from divergent neurodevelopmental trajectories which modulate electrophysiological activity and may predispose some individuals to approach substance use in a riskier way 58 , 59 . Interestingly, the increases in beta band and the reduction in the inhibition-excitation index described in this study may resemble an early pseudomaturation state, previously noted in the literature as a risk profile 11 , 60 . 4.3 Sex influence on neurodevelopment and alcohol initiation Once puberty begins, a cascade of neuroanatomical and functional changes occurs, emerging sex-related dimorphisms consistently reported in the literature 22 , 23 , 61 . Current findings align with this framework, indicating that the association between alcohol consumption and electrophysiology show a differential relationship according to sex. In this scenario, females with a prominently inhibitory cortical activity would present a higher level of alcohol consumption possibly linked to early pseudomaturation processes that facilitate reward-seeking and risk-taking behaviors, such as alcohol misuse. To our knowledge, no prior studies have specifically explored sex differences in electrophysiology at pre-consumption stages. However, a recent review by Almeida-Antunes et al., (2021) indicated that studies with university heavy drinkers do not detect consistent electrophysiological differences between sexes. These assumptions are consistent with the second phase of our study, where the moderating effect of sex on the beta-consumption relationship disappeared two years after. This phenomenon may be attributed to the asynchrony of the maturation trajectories between males and females, which tends to converge during this period 22 . On the other hand, our results may stem from varying factors underlying alcohol use between sexes. In males, higher levels of sensation-seeking, likely driven by peak testosterone levels during adolescence, appear to promote consumption behaviours 62 . In contrast, consumption in females seems linked to earlier and distinct maturational changes at structural and physiological level potentially influenced by estradiol effects on their nervous system. 4.4 Strengths and limitations The current study has several limitations that should be addressed in future research. First, although our results align with the hypothesis of pubertal differences, we lack specific measures of pubertal stage in our sample. This information is crucial given current evidence and should be collected in future studies. Second, the study collected data at two time points—early and mid-adolescence. While this is sufficient to identify predisposition factors and its evolution, incorporating a third time point in late adolescence would allow for a more precise characterization of the electrophysiological maturation. Nonetheless, this study's strength lies in its longitudinal approach, utilizing robust and cutting-edge methodologies to explore the electrophysiological phenotypes associated with adolescent alcohol consumption. 5. Conclusion In conclusion, spontaneous electrophysiological activity may represent an early biomarker of alcohol consumption initiation years later and is related with the appearance of excitation-inhibition imbalance. Contrary to cross-sectional studies in young binge drinkers, some of this trait arises form maturational changes rather than effects of alcohol drinking itself. Moreover, this predisposition factors towards initiation in alcohol consumption shows sex-dependent impact in adolescence behaviours. These differences show distinctive physiological and psychological correlates for males and females, which should be further explored. For that reason, the development of preventive strategies should consider these individual particularities to efficiently reach to young people needs and motivations. Declarations 6. Data availability Processed data for power and f E/I and scripts for statistics are publicly available on the OSF website (https://osf.io/dtb2u/). Raw data can be accessed through a data transfer agreement with the responsible university (Universidad Complutense de Madrid). Finally, the MEG signal preprocessing and cleaning codes can be found publicly available on the Github repository (https://github.com/rbruna/meeg_analysis). 7. Aknowledgments Author Contributions: Luis Fernando Antón-Toro and Alberto del Cerro-León had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Luis Fernando Antón-Toro, Fernando Maestú and Luis M García Moreno. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Alberto del Cerro-León. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Alberto del Cerro-León and Pablo Cuesta. Obtained funding: Fernando Maestú and Luis M García-Moreno Supervision: Luis Fernando Antón-Toro. Conflict of Interest Disclosures: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding/Support: The funds for the conduct of this research have been granted by the Plan Nacional sobre Drogas in the 2014 (PR2014), 2017 (PNSD2017|039) and 2021 (PNSD2021|075) calls of the Ministerio de Sanidad of Spain. References Casey BJ, Jones RM, Somerville LH. Braking and Accelerating of the Adolescent Brain. J Res Adolesc . 2011;21(1):21-33. doi:10.1111/j.1532-7795.2010.00712.x Blakemore SJ, Choudhury S. 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Front Behav Neurosci . 2020;14(May):1-21. doi:10.3389/fnbeh.2020.00074 Zallar LJ, Rivera-Irizarry JK, Hamor PU, et al. Rapid nongenomic estrogen signaling controls alcohol drinking behavior in mice. Nat Commun . 2024;15(1):10725. doi:10.1038/s41467-024-54737-6 Guillamón C, Solé G, Farran C. Test para la identificación de transtornos por uso de alcohol (AUDIT): Traducción y validación del AUDIT al catalán y castellano. Adicciones . 1999;11(1):347. https://search.proquest.com/docview/1609163728?pq-origsite=gscholar Martínez-Loredo V, Fernández-Hermida JR, Fernández-Artamendi S, Carballo JL, García-Rodríguez O. Spanish adaptation and validation of the Barratt Impulsiveness Scale for early adolescents (BIS-11-A). Int J Clin Heal Psychol . 2015;15(3):274-282. doi:10.1016/j.ijchp.2015.07.002 Zuckerman M. The sensation seeking scale V (SSS-V): Still reliable and valid. Pers Individ Dif . 2007;43(5):1303-1305. doi:10.1016/j.paid.2007.03.021 Barkley RA. Barkley Deficits in Executive Functioning Scale—Children and Adolescents (BDEFS-CA). The Guilford Press; 2012. Gioia GA, Isquith PK, Guy SC, Kenworthy L. TEST REVIEW Behavior Rating Inventory of Executive Function. Child Neuropsychol . 2000;6(3):235-238. doi:10.1076/chin.6.3.235.3152 Pedrero Pérez EJ, Ruiz Sánchez De León JM, Rojo Mota G, et al. Versión española del Cuestionario Disejecutivo (DEX-Sp): propiedades psicométricas en adictos y población no clínica. Adicciones . 2009;21(2):155. doi:10.20882/adicciones.243 Taulu S, Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys Med Biol . 2006;51(7):1759-1768. doi:10.1088/0031-9155/51/7/008 Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci . 2011;2011. doi:10.1155/2011/156869 Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage . 2002;15(1):273-289. doi:10.1006/nimg.2001.0978 Nolte G. The magnetic lead field theorem in the quasi-static approximation and its use for magnetoenchephalography forward calculation in realistic volume conductors. Phys Med Biol . 2003;48(22):3637-3652. doi:10.1088/0031-9155/48/22/002 Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods . 2007;164(1):177-190. doi:10.1016/j.jneumeth.2007.03.024 Bruining H, Hardstone R, Juarez-Martinez EL, et al. Measurement of excitation-inhibition ratio in autism spectrum disorder using critical brain dynamics. Sci Rep . 2020;10(1):1-15. doi:10.1038/s41598-020-65500-4 Hayes AF. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. In: Guilford Press. ; 2013. Correas A, Rodriguez Holguín S, Cuesta P, et al. Exploratory Analysis of Power Spectrum and Functional Connectivity During Resting State in Young Binge Drinkers: A MEG Study. Int J Neural Syst . 2015;25(03):1550008. doi:10.1142/S0129065715500082 Rangaswamy M, Porjesz B, Chorlian DB, et al. Beta power in the EEG of alcoholics. Biol Psychiatry . 2002;52(8):831-842. doi:10.1016/S0006-3223(02)01362-8 Betti V, Della Penna S, de Pasquale F, Corbetta M. Spontaneous Beta Band Rhythms in the Predictive Coding of Natural Stimuli. Neuroscientist . 2021;27(2):184-201. doi:10.1177/1073858420928988 Engel AK, Fries P. Beta-band oscillations-signalling the status quo? Curr Opin Neurobiol . 2010;20(2):156-165. doi:10.1016/j.conb.2010.02.015 Marco-Pallarés J, Münte TF, Rodríguez-Fornells A. The role of high-frequency oscillatory activity in reward processing and learning. Neurosci Biobehav Rev . 2015;49:1-7. doi:10.1016/j.neubiorev.2014.11.014 Gruber AJ, Calhoon GG, Shusterman I, Schoenbaum G, Roesch MR, O’Donnell P. More Is Less: A Disinhibited Prefrontal Cortex Impairs Cognitive Flexibility. J Neurosci . 2010;30(50):17102-17110. doi:10.1523/JNEUROSCI.4623-10.2010 Newson JJ, Thiagarajan TC. EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies. Front Hum Neurosci . 2019;12. doi:10.3389/fnhum.2018.00521 Abrahao KP, Salinas AG, Lovinger DM. Alcohol and the Brain: Neuronal Molecular Targets, Synapses, and Circuits. Neuron . 2017;96(6):1223-1238. doi:10.1016/j.neuron.2017.10.032 Nimitvilai S, Lopez MF, Mulholland PJ, Woodward JJ. Chronic Intermittent Ethanol Exposure Enhances the Excitability and Synaptic Plasticity of Lateral Orbitofrontal Cortex Neurons and Induces a Tolerance to the Acute Inhibitory Actions of Ethanol. Neuropsychopharmacology . 2016;41(4):1112-1127. doi:10.1038/npp.2015.250 Correas A, Cuesta P, Rosen BQ, Maestu F, Marinkovic K. Compensatory neuroadaptation to binge drinking: Human evidence for allostasis. Addict Biol . 2021;26(3). doi:10.1111/adb.12960 Hunt BAE, Wong SM, Vandewouw MM, Brookes MJ, Dunkley BT, Taylor MJ. Spatial and spectral trajectories in typical neurodevelopment from childhood to middle age. Netw Neurosci . 2019;3(2):497-520. doi:10.1162/netn_a_00077 Hunt BAE, Tewarie PK, Mougin OE, et al. Relationships between cortical myeloarchitecture and electrophysiological networks. Proc Natl Acad Sci U S A . 2016;113(47):13510-13515. doi:10.1073/pnas.1608587113 Tewarie P, Hillebrand A, van Dellen E, et al. Structural degree predicts functional network connectivity: A multimodal resting-state fMRI and MEG study. Neuroimage . 2014;97:296-307. doi:10.1016/j.neuroimage.2014.04.038 Segalowitz SJ, Santesso DL, Jetha MK. Electrophysiological changes during adolescence: A review. Brain Cogn . 2010;72(1):86-100. doi:10.1016/j.bandc.2009.10.003 Kamarajan C, Porjesz B. Advances in electrophysiological research. Alcohol Res Curr Rev . 2015;37(1). Walters RK, Polimanti R, Johnson EC, et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci . 2018;21(12):1656-1669. doi:10.1038/s41593-018-0275-1 Squeglia LM, Ball TM, Jacobus J, et al. Neural predictors of initiating alcohol use during adolescence. Am J Psychiatry . 2017;174(2):172-185. doi:10.1176/appi.ajp.2016.15121587 Witt ED. Puberty, hormones, and sex differences in alcohol abuse and dependence. Neurotoxicol Teratol . 2007;29(1):81-95. doi:10.1016/j.ntt.2006.10.013 Seo S, Beck A, Matthis C, et al. Risk profiles for heavy drinking in adolescence: differential effects of gender. Addict Biol . 2019;24(4):787-801. doi:10.1111/adb.12636 Additional Declarations There is NO Competing Interest. 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The figure highlights the progression of cluster morphology (shaded in red) across frequency steps. Axial brain slices are presented in MNI coordinates.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6057213/v1/44d62ebcc485bfd78a1edf49.jpg"},{"id":79239974,"identity":"a3c21518-817e-4060-b0c4-7e84365a5515","added_by":"auto","created_at":"2025-03-26 05:32:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203584,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between low-beta (12 to 20 Hz) spectral power and SAUs during the second phase of the study. The figure highlights the progression of cluster morphology (shaded in red) across frequency steps. Axial brain slices are presented in MNI coordinates.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6057213/v1/1ccf375a0d3a7b0e3088da9d.jpg"},{"id":79241673,"identity":"53365ba0-e102-4cac-91ca-4ca4cd5cd38e","added_by":"auto","created_at":"2025-03-26 06:04:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71863,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the power in the low beta band and the \u003cem\u003ef\u003c/em\u003eE/I in the clusters identified in the CBPT analysis. The individual values and the regression line of the relationship for the first phase of the study are shown in blue. The individual values and the regression line of the ratio for the second phase of the study are shown in purple.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6057213/v1/e4b241fba2aa9f8f42c2b3ca.jpg"},{"id":79793045,"identity":"64d2f49d-50ac-4b21-b4f5-ea2580936019","added_by":"auto","created_at":"2025-04-02 20:24:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1564451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6057213/v1/0b62c0f4-b2c3-4be7-a78b-064bdca96eb8.pdf"},{"id":79239977,"identity":"646b904c-15d8-4f18-bd72-533847a6864c","added_by":"auto","created_at":"2025-03-26 05:32:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":171246,"visible":true,"origin":"","legend":"Supplementary figure","description":"","filename":"Supplement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6057213/v1/e1adeb42d79507a2782919f6.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Electrophysiological sex-dimorphism as early risk markers of alcohol use in adolescence","fulltext":[{"header":"Significance statement","content":"\u003cp\u003eAlcohol consumption during adolescence is associated with alterations in brain activity. To differentiate the effects of alcohol from predisposing traits, a longitudinal study was carried out which demonstrated the existence of anomalies in the frequency spectrum in stages prior to consumption and which were maintained 2 years later. Furthermore, it was found that these differences were associated with changes in inhibitory tone that occur during adolescence. Finally, moderation analyses revealed that the associations between brain activity and alcohol consumption were influenced by sex, being especially relevant in females.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe last 20 years of animal, neuroimaging, and behavioural research have demonstrated that adolescence is a critical period of brain development, characterized by changes in both brain structure and function\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and directly involved in higher-order and complex cognition\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. During this period, adolescents are particularly sensitive to rewarding effects, which may lead to risky behaviours such as substances misuse that impact on social, psychological, and neurobiological development\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In this regard, researchers has demonstrated that alcohol use is linked to alterations in electrophysiological activity such as increased theta and beta band power in frontal regions \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and posterior occipito-temporal cortices\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Similarly, functional connectivity studies have also highlighted disrupted resting-state networks associated to alcohol HD\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, growing evidence underscores the need for prospective longitudinal studies to differentiate neurobiological factors predisposing individuals to HD from those resulting from consumption\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, no studies have yet characterized spontaneous power spectra in relation to alcohol use onset during adolescence development.\u003c/p\u003e \u003cp\u003eThe onset of puberty involves several neurobiological changes that leave a distinctive fingerprint on spontaneous electrophysiological activity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. During normative development, synaptic pruning and interneuron maturation in the brain\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e lead to faster power spectral density oscillations, particularly in the alpha, beta, and gamma bands\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, internal factors such as sex hormones have a critical role causing significant sex-dimorphisms in the neurological maturation\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In males, cortical maturation influenced by testosterone occurs gradually and prolonged in time. In contrast, female\u0026rsquo;s puberty begins earlier and progress faster due to earlier increases in estradiol\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, with a more pronounced increase in beta-band activity\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In turn. recent studies, such as McSweeney et al. (2021), have also demonstrated sex-dependent maturation in aperiodic brain activity, an index of the excitatory/inhibitory (E/I) balance. This differential brain maturation due to sex-related factors further influences the reward system, potentially shaping the development of substance consumption habits. In males, testosterone targets the nucleus accumbens, promoting risk-taking behaviours\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, whereas in females, estradiol amplifies dopaminergic release, leading to heightened vulnerability to the rewarding effects of substances\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThis prospective longitudinal study\u003c/b\u003e aimed to distinguish between neurobiological factors that predispose individuals to heavy drinking and those resulting from alcohol consumption itself. We hypothesize that some electrophysiological differences in power spectra previously reported in young adults with binge drinking (BD) are present \u003cb\u003ebefore\u003c/b\u003e the onset of alcohol consumption, serving as predisposition markers. Furthermore, these differences in power spectra might emerge from alterations in the excitation-inhibition (E/I) balance and influenced by biological sex. We conducted three analyses within a longitudinal study framework. First, we used magnetoencephalography (MEG) to measure resting-state brain power spectra in a cohort of adolescents \u003cb\u003eboth before and after\u003c/b\u003e engaging in heavy drinking (HD). Next, we explored the relationship between these electrophysiological profiles and the E/I balance to better understand their underlying neural mechanisms. Finally, given known sex differences in neurodevelopment, we examined the effect of biological sex on these brain activity profiles.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Participants\u003c/h2\u003e\n \u003cp\u003eThe participants were recruited from two projects funded by the Spanish Ministry of Health in 2015 and 2019. Samples were collected from different high schools across the community of Madrid, following an identical evaluation protocol in two stages separated by a two-year follow-up period. Participants were screened to ensure no history of alcohol consumption, family alcohol use and psychiatric or neurological disorders. All participants completed the Alcohol Use Disorder Identification Test (AUDIT) \u003csup\u003e31\u003c/sup\u003e and a semi-structured interview regarding substance use habits. In the first stage, before the onset of alcohol use, 148 adolescents participated in a Magnetoencephalography (MEG) recording of 5 minutes in resting-state with eyes closed. 121 of those participants also underwent magnetic resonance imaging (MRI) study. After the two-year follow-up period, 73 participants were re-evaluated. Based on AUDIT and interview information, we calculated the quantity of Standard Alcohol Units (SAUs) (1 SAUs = 10mg ethanol) consumed during regular drinking episodes, considering the number and type of beverages consumed within 2–3-hour. Tobacco and cannabis use were monitored, and 2 participants with regular use were excluded. We used self-report scales to evaluate traits of impulsivity (Barratt Impulsiveness Scale \u003csup\u003e32\u003c/sup\u003e (BIS-11)), sensation seeking (SSS-V \u003csup\u003e33\u003c/sup\u003e), and dysexecutive behaviors (Barkley Deficits in Executive Functioning Scale \u003csup\u003e34\u003c/sup\u003e (BDEFS), the Behavior Rating Inventory of Executive Function\u003csup\u003e35\u003c/sup\u003e (BRIEF), and the Dysexecutive Questionnaire \u003csup\u003e36\u003c/sup\u003e (DEX)). After quality control of the MEG and MRI data, a final sample of 55 subjects (32 males and 23 females) completed the protocol. Informed consent was obtained from all participants and their parents or legal guardians in accordance with the Declaration of Helsinki and the study received ethical approval from the ethics committee of the Universidad Complutense de Madrid.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 MRI recordings and volumetry\u003c/h2\u003e\n \u003cp\u003eThe participants underwent a high-resolution 3D T1-weighted brain MRI scan at either the Santa Elena Foundation (General Electric Optima MR450w, 1.5 T) or the Clinical Hospital of Madrid (General Electric Signa HDxt, 1.5 T). The scan parameters for both machines included echo time: 4.2 ms, repetition time: 11.2 ms, inversion time: 450 ms, field of view: 100, acquisition matrix: 256 × 256, and slice thickness: 1 mm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 MEG recordings\u003c/h2\u003e\n \u003cp\u003eMEG data were acquired using a 306-channel (102 magnetometers and 204 planar gradiometers) whole head Elekta Neuromag system located in a magnetically shielded room at the Center for Biomedical Technology in Madrid, Spain. Brain activity was recorded during an eyes-closed resting state using an online FIR-type anti-alias filter with a frequency range of 0.1 to 330 Hz and a sampling rate of 1,000 Hz. The head shape of each participant was captured using a Fastrak digitizer (Polhemus, Colchester, Vermont) with three fiducial landmarks (nasion and left and right pre-auricular points). Four Head Position Indicator (HPI) coils were attached to the participant’s scalp to track head position. Additionally, two sets of bipolar electrodes were used to monitor eye blinks and heartbeats.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2. 4 Signal processing and source-space reconstruction\u003c/h3\u003e\n\u003cp\u003eTo reduce environmental noise and correct for subject movements, a temporal extension of the signal space separation (tSSS) method was applied \u003csup\u003e37\u003c/sup\u003e. The Fieldtrip software \u003csup\u003e38\u003c/sup\u003e in Matlab R2020b was used to automatically detect artifacts in the MEG signal, which were then visually confirmed by an MEG expert and the artifact-free data were segmented into 4-second epochs with 2 additional seconds of real data on either side as padding.\u003c/p\u003e\n\u003cp\u003eIndividual MEG signals were estimated at the source level using participant's T1-weighted MRI using an homogeneous grid based on the Automated Anatomical Labeling (AAL) atlas \u003csup\u003e39\u003c/sup\u003e. This grid comprised 1202 positions corresponding to 78 cortical regions, which were transformed into the participant's space using a linear transformation between the MNI template and the participant's T1-weighted MRI. Additionally, the T1-weighted image was employed to create a single-shell head model based on the inner surface of the skull \u003csup\u003e40\u003c/sup\u003e. Finally, a linearly constrained minimum variance (LCMV) beamformer was applied as the inverse method to reconstruct the signal in the cortical sources.\u003c/p\u003e\n\u003cp\u003eAnalysis 1: Characterization of power-spectra profiles related to alcohol use\u003c/p\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 Power spectra calculations\u003c/h2\u003e\n \u003cp\u003eThe power spectrum at each source was computed using the Fieldtrip toolbox \u003csup\u003e38\u003c/sup\u003e and a multitaper method (mtmfft) with discrete prolate spheroidal sequences (dpss) as the windowing function and 1 Hz smoothing. We analysed relative power by normalizing each frequency step by the total power across the entire spectrum in the 2–45 Hz range. This resulted in a source-reconstructed matrix with dimensions of 1202 nodes x 173 frequency steps x 55 participants.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.6 Statistical analysis: Cluster based permutation test (CBPT)\u003c/h2\u003e\n \u003cp\u003eTo identify power spectra patterns related to SAUs, we performed a cluster-based permutation test (CBPT) implemented in Fieldtrip \u003csup\u003e38,41\u003c/sup\u003e. Firstly, Spearman’s partial correlation tests between normalized power across different frequency bands (theta = 4-8Hz, alpha = 8-12Hz, low-beta = 12-20Hz, high-beta = 20-30Hz and gamma = 30-45Hz) and SAUs were calculated per node controlling for age, sex and project. Rho values at p \u0026lt; 0.05 where thresholded and clustered considering adjacent nodes in space-frequency. Clusters must exceed 1% of total nodes and show significance in three consecutive frequencies. Then, rho values were converted to Fisher’s Z, computing the cluster mass statistics as the sum of Z values. To control for multiple comparison, the statistical analysis was repeated 50000 times with shuffled data to create a null distribution. Once calculated, the empirical distribution allowed us to calculate the p-value corresponding to each of the original candidate clusters. Finally, clusters with CBPT p \u0026lt; 0.05 were considered for further analysis.\u003c/p\u003e\n \u003cp\u003eAnalysis 2: Correlation between power spectra and excitation-inhibition ratio ( f E/I)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.7 Functional excitation-inhibition ratio\u003c/h2\u003e\n \u003cp\u003eTo estimate the excitation-inhibition ratio (\u003cem\u003ef\u003c/em\u003eE/I) we applied the algorithm developed by Bruining et al., 2020. The segmented source-space signals were converted into continuous time-series by concatenating segments and interpolating over artifact-disconnected gaps. The signal profile for each source was defined as the cumulative sum of the demeaned amplitude envelope, segmented into 5-second windows with 80% overlap. Each window was amplitude-normalized, detrended, and its standard deviation calculated. fE/I values were estimated in 1202 sources within the low beta band (12–20 Hz) using the Pearson correlation between windowed mean amplitudes and standard deviations. Interpretation: fE/I \u0026gt; 1 indicates excitation dominance, fE/I \u0026lt; 1 inhibition dominance, and fE/I ≠ 1 a balanced system. Detrended fluctuation analysis (DFA) was applied, and mean fE/I values per participant were averaged across CBPT clusters with DFA exponents \u0026gt; 0.55 to exclude unreliable values.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.8 Statistical analysis: Partial correlation\u003c/h2\u003e\n \u003cp\u003eTo analyze the association between the power-spectra and levels of \u003cem\u003ef\u003c/em\u003eE/I, Spearman’s partial correlations were calculated for each stage of the study, controlling for the effects of sex, age and project.\u003c/p\u003e\n \u003cp\u003eAnalysis 3: Sex effect in the characterization of power spectra profiles\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e2.9 Statistical analysis: Moderation\u003c/h2\u003e\n \u003cp\u003eModeration effects were assessed using the Statistical Package for the Social Sciences (SPSS) 29.0.2.0 and the macro \u003cem\u003eProcess\u003c/em\u003e\u003csup\u003e43\u003c/sup\u003e version 4.3 (www.processmacro.org/index.html). The relative power of the cluster was used as predictor, the SAUs as dependent variable and sex as moderator. In addition, age was included as a covariate to eliminate its effect on the analysis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographics\u003c/h2\u003e \u003cp\u003eAfter the follow-up period, adolescents had an average alcohol consumption of 3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 SAUs with no significant difference between males and females. Regarding behavioural variables, differences were shown in sensation seeking during the first phase of the study (p-value\u0026thinsp;=\u0026thinsp;0.017), where men had higher scores on the SSS-V questionnaires (19\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1) compared to their female counterparts (14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3). The means and standard deviations of the whole sample and both sexes as well as the differences between groups are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eAlcohol consumption, age and cognitive scale scores over the course of the study.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003eWhole (55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale (32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale (23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,9 (2,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,9 (2,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,9 (2,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e1st Phase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,5 (0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,5 (0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,4 (0,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS_V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e17,2 (6,5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e19,0 (6,1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e14,8 (6,3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,02*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIS_11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58,2 (14,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56,1 (13,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61,0 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCA_exe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (25,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,0 (28,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,3 (22,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e2nd Phase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,4 (0,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,5 (0,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,3 (0,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS_V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,62 (5,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,3 (5,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,8 (4,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIS_11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63,0 (8,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62,2 (8,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64,1 (8,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCA_exe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (23,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4,6 (21,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,74 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003e*\u003c/b\u003e Marks significant differences between males and females.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eExperiment 1: Power-spectra relates to Alcohol use\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Pre-consumption MEG\u003c/h2\u003e \u003cp\u003eCBPT analysis reveals a positive correlation between future alcohol consumption and spectral power between 12 to 18.50 Hz (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At lower frequencies, the cluster encompass the bilateral temporal lobes, expands with increasing frequency to occipital and frontal regions, to finally be reduced to a few frontal and occipital sources at higher frequencies. Spearman\u0026rsquo;s rho ranged between 0.23 and 0.46, with an averaged statistical parameter of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042 and \u003cem\u003erho\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.34.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Post-consumption MEG\u003c/h2\u003e \u003cp\u003eTwo years later, positive correlation maintained a significant association with SAUs (CBPT p-value\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003erho\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37) between 12 to 20 Hz (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with Spearman\u0026rsquo;s rho ranged between 0.23 and 0.55. At lower frequencies, the cluster were limited to bilateral temporal regions, extended toward occipital, parietal and inferior frontal regions, and finally were reduced to occipital sources at higher frequencies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eExperiment 2: More low-beta power is associated with lower\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ef\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eE/I values\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe mean duration of the time-series was 281.55 s and surpassed 180.25 s in all cases. After correcting the \u003cem\u003ef\u003c/em\u003eE/I values by the DFA exponents, an average of 80.81% of the sources were marked as reliable during the first phase of the study and 88.19% during the second phase, constituting a representative measure of the excitation-inhibition balance. Once calculated, the \u003cem\u003ef\u003c/em\u003eE/I values were correlated with cluster power obtained in the first stage of the study, finding a significant negative association (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Spearman\u0026rsquo;s rho = -0.57). Moreover, this association was maintained during the second stage of the study (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Spearman\u0026rsquo;s rho = -0.42) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, \u003cem\u003ef\u003c/em\u003eE/I values during the first phase of the study also showed a positive correlation with future consumption (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Spearman\u0026rsquo;s rho = -0.34).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eExperiment 3: Sex moderates the relationship between power spectra and alcohol use\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eModeration analysis revealed a significant interaction between sex and relative power during the first stage of the study, where the importance of prediction was relevant only in the female group (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Spearman\u0026rsquo;s rho\u0026thinsp;=\u0026thinsp;0.66). Moderation analyses during the second stage found a predictive value of alcohol on relative power without significant interaction with sex. A detailed description of the models is depicted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The partial correlations between relative power and consumption in the whole sample and in each of the sexes can be found in \u003cb\u003eSupplementary material 1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModeration analysis of sex in the prediction of SAUs\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e1st Phase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow beta power (Z-scored)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex * Low beta power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2,42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConditional effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e2nd Phase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow beta power (Z-scored)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0,01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex * Low beta power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0,99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0,25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Summary of the main findings\u003c/h2\u003e \u003cp\u003eThis study aimed to characterize the electrophysiological power-spectra and excitation-inhibition balance associated with heavy drinking (HD) onset, and its impact on brain activity during adolescence. Additionally, we explored how biological sex moderate this association. Findings revealed that increased beta-band power in the frontal, temporal, and parieto-occipital cortices was related to higher alcohol consumption two years later. A similar pattern emerged after drinking onset, with higher beta-band power linked to increased consumption rates. These electrophysiological features were associated with lower excitation-inhibitory ratio and strongly moderated by sex.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Alterations in power due to heavy alcohol consumption\u003c/h2\u003e \u003cp\u003ePrevious literature reported that HD consumption in university young adults cause an increase in beta band power during resting-state EEG, particularly in frontal and temporal regions \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and decreases in low frequencies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Such results were explained as a compensatory mechanism, according to which, the brain allocates a higher resources to compensate for underlying neural deficits\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Notably, these alterations resemble those observed in alcohol-dependent individuals, suggesting that HD may share similar neurotoxic effects\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. However, current results suggest that electrophysiological differences in beta-band exist prior to consumption and persist throughout adolescence following initiation of alcohol use. This evidence aligns with prior findings that suggest the existence of functional connectivity profiles prone to consumption\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, no associations were found between alcohol consumption and alpha or theta power at any stage. This suggests that decreased slow-band power may result from prolonged consumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Implications of excitation-inhibition balance in the beta band\u003c/h2\u003e \u003cp\u003eBeta rhythm is widely recognized for its broad involvement in cognitive processes and brain dynamics\u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. During resting state, it is proposed to facilitate the integration of resting-state networks and support the brain\u0026rsquo;s global efficiency\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. On the other hand, excessive beta band has been associated with reduced cognitive flexibility, ruminative thoughts, compulsive behaviours\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, and addiction disorders\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In light of recent results, it has proposed that increases in beta band reflect an excitatory-inhibitory imbalance potentially linked to alterations in the GABAergic and glutamatergic neurotransmitter systems\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In accordance with these hypotheses, animal and human studies have shown that continued alcohol use causes a decrease in GABAa receptors and an excitatory upregulation\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Conversely, in our results, increases in beta power were associated with more inhibition-prone ratio. In this regard, during adolescent neural development changes in electrophysiological activity are a common phenomenon\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e due to cortical circuitry transformations\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. This process is influenced by facilitating factors, such as synaptic pruning and the maturation of PV\u0026thinsp;+\u0026thinsp;interneurons, which facilitated local evoked activity and enhance neuronal plasticity\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Meanwhile, myelin band development stabilizes neural networks and increases transmission speed between cortical regions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Taken together, these developmental changes promote shifts in electrophysiological activity towards faster-frequency bands, particularly in posterior regions and the precentral gyrus\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Thus, differences prior to alcohol consumption may arise from divergent neurodevelopmental trajectories which modulate electrophysiological activity and may predispose some individuals to approach substance use in a riskier way\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Interestingly, the increases in beta band and the reduction in the inhibition-excitation index described in this study may resemble an early pseudomaturation state, previously noted in the literature as a risk profile\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Sex influence on neurodevelopment and alcohol initiation\u003c/h2\u003e \u003cp\u003eOnce puberty begins, a cascade of neuroanatomical and functional changes occurs, emerging sex-related dimorphisms consistently reported in the literature\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Current findings align with this framework, indicating that the association between alcohol consumption and electrophysiology show a differential relationship according to sex. In this scenario, females with a prominently inhibitory cortical activity would present a higher level of alcohol consumption possibly linked to early pseudomaturation processes that facilitate reward-seeking and risk-taking behaviors, such as alcohol misuse. To our knowledge, no prior studies have specifically explored sex differences in electrophysiology at pre-consumption stages. However, a recent review by Almeida-Antunes et al., (2021) indicated that studies with university heavy drinkers do not detect consistent electrophysiological differences between sexes. These assumptions are consistent with the second phase of our study, where the moderating effect of sex on the beta-consumption relationship disappeared two years after. This phenomenon may be attributed to the asynchrony of the maturation trajectories between males and females, which tends to converge during this period\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. On the other hand, our results may stem from varying factors underlying alcohol use between sexes. In males, higher levels of sensation-seeking, likely driven by peak testosterone levels during adolescence, appear to promote consumption behaviours\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In contrast, consumption in females seems linked to earlier and distinct maturational changes at structural and physiological level potentially influenced by estradiol effects on their nervous system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Strengths and limitations\u003c/h2\u003e \u003cp\u003eThe current study has several limitations that should be addressed in future research. First, although our results align with the hypothesis of pubertal differences, we lack specific measures of pubertal stage in our sample. This information is crucial given current evidence and should be collected in future studies. Second, the study collected data at two time points\u0026mdash;early and mid-adolescence. While this is sufficient to identify predisposition factors and its evolution, incorporating a third time point in late adolescence would allow for a more precise characterization of the electrophysiological maturation. Nonetheless, this study's strength lies in its longitudinal approach, utilizing robust and cutting-edge methodologies to explore the electrophysiological phenotypes associated with adolescent alcohol consumption.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, spontaneous electrophysiological activity may represent an early biomarker of alcohol consumption initiation years later and is related with the appearance of excitation-inhibition imbalance. Contrary to cross-sectional studies in young binge drinkers, some of this trait arises form maturational changes rather than effects of alcohol drinking itself. Moreover, this predisposition factors towards initiation in alcohol consumption shows sex-dependent impact in adolescence behaviours. These differences show distinctive physiological and psychological correlates for males and females, which should be further explored. For that reason, the development of preventive strategies should consider these individual particularities to efficiently reach to young people needs and motivations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Data availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProcessed data for power and \u003cem\u003ef\u003c/em\u003eE/I and scripts for statistics are publicly available on the OSF website (https://osf.io/dtb2u/). Raw data can be accessed through a data transfer agreement with the responsible university (Universidad Complutense de Madrid). Finally, the MEG signal preprocessing and cleaning codes can be found publicly available on the Github repository (https://github.com/rbruna/meeg_analysis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Aknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Luis Fernando Ant\u0026oacute;n-Toro and Alberto del Cerro-Le\u0026oacute;n had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eConcept and design: Luis Fernando Ant\u0026oacute;n-Toro, Fernando Maest\u0026uacute; and Luis M Garc\u0026iacute;a Moreno.\u003c/li\u003e\n \u003cli\u003eAcquisition, analysis, or interpretation of data: All authors.\u003c/li\u003e\n \u003cli\u003eDrafting of the manuscript: Alberto del Cerro-Le\u0026oacute;n.\u003c/li\u003e\n \u003cli\u003eCritical revision of the manuscript for important intellectual content: All authors.\u003c/li\u003e\n \u003cli\u003eStatistical analysis: Alberto del Cerro-Le\u0026oacute;n and Pablo Cuesta.\u003c/li\u003e\n \u003cli\u003eObtained funding: Fernando Maest\u0026uacute; and Luis M Garc\u0026iacute;a-Moreno\u003c/li\u003e\n \u003cli\u003eSupervision: Luis Fernando Ant\u0026oacute;n-Toro.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosures:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding/Support:\u003c/strong\u003e The funds for the conduct of this research have been granted by the Plan Nacional sobre Drogas in the 2014 (PR2014), 2017 (PNSD2017|039) and 2021 (PNSD2021|075) calls of the Ministerio de Sanidad of Spain.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCasey BJ, Jones RM, Somerville LH. 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Risk profiles for heavy drinking in adolescence: differential effects of gender. \u003cem\u003eAddict Biol\u003c/em\u003e. 2019;24(4):787-801. doi:10.1111/adb.12636\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6057213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6057213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeavy drinking during adolescence is associated to alterations in the electrophysiology of the brain. However, some of these alterations are observable at pre-consumption stages. To identify the brain activity profiles associated with alcohol consumption and to address its causes, we examined the power spectra and functional excitation-inhibition ratio in a cohort of 55 adolescents within a two-stage study separated by a two-year follow-up. Our results indicate that power-spectra in beta-band showed a positive correlation with alcohol use during both phases of the study (1st: rho\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;0.042; 2nd: rho\u0026thinsp;=\u0026thinsp;0.37; p\u0026thinsp;=\u0026thinsp;0.02) and a negative correlation with excitation-inhibition ratio (1st: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; rho=-0.30; 2nd: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; rho = -0.43). Finally, biological sex showed strong moderation effect, were females drive the predictive relationship (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; rho\u0026thinsp;=\u0026thinsp;0.66; β=-0,61). Taken together, these results indicate that electrophysiological differences prior to consumption are predictive of future alcohol use in females and associated with activity profiles prone to inhibition.\u003c/p\u003e","manuscriptTitle":"Electrophysiological sex-dimorphism as early risk markers of alcohol use in adolescence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 05:31:59","doi":"10.21203/rs.3.rs-6057213/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"890dcfa7-74be-477d-aee8-330886357df3","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44815481,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":44815482,"name":"Biological sciences/Physiology/Neurophysiology"}],"tags":[],"updatedAt":"2025-04-02T20:17:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 05:31:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6057213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6057213","identity":"rs-6057213","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}