Early abstinence in severe alcohol use disorder: MCP-1 decline, choroid plexus shrinkage, and region-specific grey-matter volume changes

Early abstinence in severe alcohol use disorder: MCP-1 decline, choroid plexus shrinkage, and region-specific grey-matter volume changes | 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 Early abstinence in severe alcohol use disorder: MCP-1 decline, choroid plexus shrinkage, and region-specific grey-matter volume changes Geraldine Petit, Selim Mohamed Kotb, Santiago Canals, Peter Starkel, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6915490/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Translational Psychiatry → Version 1 posted 12 You are reading this latest preprint version Abstract In this longitudinal study, 37 patients with severe alcohol use disorder (SAUD) were tested at the beginning (T1) and end (T2) of withdrawal to explore the evolution of and relationship between systemic inflammation, volumetric changes for brain grey matter (GM) and choroid plexus (ChP), and clinical symptoms. At T1, patients exhibited high levels of anxiety, depression, and craving, and had elevated plasmatic pro-inflammatory cytokines, indicating low-grade systemic inflammation. MCP-1 levels correlated positively with ChP volume and severity of withdrawal symptoms, while MIP-1β also correlated with ChP volume, together suggesting an acute immune response at T1, and underscoring the ChP as a potential neuroimmune biomarker. During three weeks’ abstinence, IL-8, MIP-1β, and MCP-1 levels decreased, although MIP-1β did not return to control levels, and TNF-α showed no significant reduction. Volumetric MRI analyses suggested two concurrent trajectories during withdrawal. First, an overall “recovery‐driven” pattern of rapidly increasing grey matter (GM) volume in widespread forebrain regions with parallel declines in ventricle size and craving. Second, GM in limbic cortical areas, temporal and inferior frontal cortex showed no significant volumetric gain. However, these regional volumes correlated significantly with declining MCP‐1, indicative of a “deflation”, potentially related to declining microglial activation. These findings highlight the role of inflammatory processes in shaping early neuroplastic changes during withdrawal and point to a central role of MCP‐1 in inflammation-driven morphometric changes. Health sciences/Biomarkers/Prognostic markers Biological sciences/Neuroscience/Molecular neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Severe alcohol use disorder (SAUD) is a major cause of preventable disability and mortality. Beyond its effects on brain structure and neurotransmission (Koob & Volkow, 2016a), increasing evidence implicates neuro-inflammation in alcohol-related brain damage (Erickson et al., 2019). In SAUD, inflammation encompasses systemic immune activation (e.g., elevated TNF-α, IL-6 due to gut-derived endotoxins), neuroinflammation (glial activation in the CNS), and microgliosis (a more specific microglial response involving morphological and functional shifts). These processes, often co-occurring, may interact through gut-brain and blood-brain barrier (BBB) dysfunction to exacerbate central nervous system (CNS) injury (4). Animal studies show that chronic alcohol exposure activates microglia, upregulates TLRs, and increases proinflammatory cytokines (e.g., IL-1β, TNF-α, MCP-1) in the hippocampus and prefrontal cortex, contributing to structural and cognitive impairments (Ahlers et al., 2015; Holloway et al., 2023). Human post-mortem studies corroborate these findings, with increased microglial markers and proinflammatory signaling in the cingulate cortex, amygdala, VTA, and orbitofrontal cortex of individuals with SAUD (9–11). Notably, Crews et al. (9) identified a phagocytic-like microglial phenotype in the orbitofrontal cortex (upregulated Iba1, CD11b, CCR2, and reduced Tmem119, SOCS3) and showed that microglial blockade in alcohol-exposed mice blunted astrocyte reactivity and oxidative stress, evidence that neuroimmune activation drives alcohol-related neurotoxicity. Despite this compelling evidence from preclinical and post-mortem research, neuroimaging findings remain inconsistent. Studies using positron emission tomography (PET) with the microglial translocator protein (TSPO) radioligand N-(2-[ 11 C]methoxybenzyl)-2-phenoxy-5-pyridinamine ([ 11 C]PBR28) report inconsistent results, with lower or unchanged TSPO binding in detoxified SAUD patients (12–14). This may reflect TSPO downregulation or microglial loss/dysfunction (“burn-out”) after chronic activation, leading to impaired neuroprotection and cognitive deficits (Hillmer et al., 2017). Conversely, acute alcohol exposure transiently increases TSPO binding in humans (16) and non-human primates (17), suggesting that neuroimmune responses to alcohol may be dynamic and time-sensitive and difficult to capture by single time-point scans. These findings underscore the need for longitudinal, multimodal designs that combine neuroimaging with peripheral biomarkers to track immune dynamics across intoxication and recovery (18). To address this, we conducted a longitudinal, test-retest study in SAUD patients over three weeks of abstinence, designed to track within-subject changes in peripheral inflammation (via circulating cytokines), brain structure (via MRI), and clinical symptoms (depression, anxiety, craving, and alcohol withdrawal severity). This early abstinence window is characterized by rapid clinical improvement (19,20) and partial normalization of peripheral cytokines (21), yet brain inflammatory processes in early abstinence are still poorly described (Parvaz et al., 2022), in part due to limitations in study design or timing. Recent MRI studies have shown evolving gray and white matter changes within the first weeks of abstinence (25,26), changes that resemble microglial activation patterns observed in animal models. However, direct evidence linking these changes to systemic inflammation or clinical outcomes in SAUD remains scarce. We also focused on the choroid plexuses (ChP), increasingly recognized as key neuroimmune interfaces at the blood-CSF barrier (27). The ChP act as immune sensors and modulators, and show structural and functional changes in other inflammatory disorders such as multiple sclerosis, Alzheimer, schizophrenia and major depression (27–30). Despite their central immunological role, they remain unexplored in the context of SAUD. To maximize interpretability, we tightly controlled the timing of MRI and cytokine assessments relative to last alcohol and food intake. This precise synchronization enabled us to better capture short-term neuroimmune dynamics and their potential role in early recovery from alcohol dependence. Methods 1. Participants We recruited SAUD patients admitted to Saint-Luc Academic Hospital (Brussels, Belgium) for a three-week detoxification program from 2015 to 2019. Diagnosis was established by psychiatric interview (DSM-5), and patients had been drinking until the day of admission or the day before. A control group matched for age and gender was available for comparison of inflammatory markers. Exclusion criteria included chronic inflammatory bowel disease, rheumatoid arthritis or other chronic inflammatory conditions, cancer, obesity (BMI>30 kg/m²), diabetes, and bariatric surgery. Patients were also excluded if they had taken antibiotics, probiotics, or prebiotics within two months, or anti-inflammatory drugs (NSAIDs/glucocorticoids) within one month before inclusion. Additional exclusions were cirrhosis, significant liver fibrosis (≥F2 on Fibroscan), any other Axis I DSM-5 disorder or major cognitive impairment (i.e., a score of ≤25 on the MMSE; (31)), use of drugs other than alcohol/nicotine, or contraindications to MRI (pregnancy, pacemaker/metal implants, claustrophobia). All patients received diazepam (40-60 mg/day) at the start of detoxification, with progressive tapering. Written informed consent was obtained from each participant after a full explanation of the study’s objectives, procedures, and possible risks. The study protocol was approved by the “Comité d’éthique Hospitalo-facultaire Saint-Luc UCLouvain” (2014/31dec/614). 2. Procedure Upon admission on Day 1, patients consenting to participate underwent an MRI scan (strictly between 6 and 8 hours after admission for all patients) and received questionnaires to evaluate their clinical symptoms. Overnight fasting blood samples were taken from all participants on Day 2 between 8:00 and 8:30 AM. These tests together constituted the evaluation at time 1 (T1). At the end of the detoxification program (Day 19), patients were re-evaluated by MRI scanning, questionnaires for clinical symptoms, and blood sample, together constituting T2 (Figure 1). 3. Clinical symptoms questionnaires Symptoms of depression, anxiety, and alcohol craving were measured using three self-report questionnaires: the Beck Depression Inventory (BDI) (32), the State-Trait Anxiety Inventory (STAI Form YA) (33), and the Obsessive-Compulsive Drinking Scale (OCDS) (34). The BDI, used in its French BDI-II version (35) comprises 21 items (maximum score 63; cutoffs: 0-11 minimal, 12-19 mild, 20-27 moderate, 28-63 severe) assessing depressive symptoms over the previous two weeks. The STAI Form YA’s state subscale, which contains 20 items scoring current state anxiety (range 20-80; 20-39 low, 40-59 moderate, 60-80 high), was used in its validated French version (36). The OCDS (14 items) captures cognitive aspects of craving in the preceding seven days; we employed a French translation (37), omitting items on current alcohol consumption, since participants were under supervised detoxification. Withdrawal intensity scores were measured by nurses using the Cushman Scale (38), which evaluates heart rate, systolic blood pressure, respiration, tremor, perspiration, agitation, and sensory disturbances (each scored 0-3, total possible 18). These ratings were conducted three times on Day 2 at six-hour intervals, with averaging to give the overall withdrawal severity. Pre-hospitalization alcohol consumption (grams/day) over the previous 30 days was quantified using the timeline follow-back method (39). 4. Biological sample collection Blood was collected in EDTA tubes and centrifuged at 1000 g for 15 minutes at 4 °C. Plasma was stored at −80 °C until the time of analysis. Plasma levels of four inflammatory cytokines (interleukin 8 (IL-8), monocyte chemoattractant protein 1 (MCP-1), macrophage inflammatory protein 1 beta (MIP-1β), and tumor necrosis factor alpha (TNF-α)) were measured by a multiplex cytokine assay (Human Bio-Plex; Bio-Rad Laboratories Inc., Hercules, CA, USA) according to instructions from the manufacturer. The selection of these four pro-inflammatory cytokines was based on reliable detection limits within our samples. The detection thresholds were as follows: IL-8 (0.4 pg/mL), MCP-1 (1.9 pg/mL), MIP-1β (3.0 pg/mL), and TNF-α (0.7 pg/mL). Plasma levels of other kit analytes fell below their respective detection levels. 5. MRI acquisition Three-dimensional (3D) anatomical images with heavy T1-weighting were obtained using a 3T Achieva scanner from Philips Healthcare, employing a 32-channel phased array head coil. Patients were instructed to stay still and were securely positioned in the head-holder coil, with the provision of soft earplugs for comfort. The 3D sequence involved a gradient echo sequence with an inversion prepulse (Turbo Field Echo) captured in the sagittal plane, with parameters as follows: TR/TE/flip angle = 9.1 ms/4.6 ms/8°, 150 slices, slice thickness = 1 mm, FOV = 220 × 197 mm², acquisition matrix = 296 × 247 (refined to 320*320), in-plane resolution = 0.81 × 0.95 mm² (acquisition) refined to 0.75 × 0.75 mm 2 , and a SENSE factor = 1.5 (for parallel imaging). The whole brain was analyzed and volumetrically segmented in the FreeSurfer image analysis suite (version 6) (http://surfer.nmr.mgh.harvard.edu/), using a series of automated steps. This final volumetric segmentation amalgamates data derived from a universal probabilistic atlas and subject-specific measured values, allowing for detailed volumetric analysis. The reference atlas derives from a training set of 40 healthy young subjects, whose brain structures were manually labeled (40–42). After careful visual examination of each completed segmentation any detected inaccuracies in geometry were corrected before proceeding in the data analysis pipeline. The only relevant inaccuracies were minor discrepancies related to skull stripping. 6. Statistical analyses We enrolled all eligible SAUD patients during the recruitment period (n = 37), in line with prior studies (n ≈ 25-40) that detected significant MRI and cytokine changes in early abstinence (21,43–45). Between‐group (T1 vs. controls) comparisons of demographics, clinical scores and cytokines used two‐sided t-tests (Levene’s/Welch’s or nonparametric as needed), and within‐patient T1-T2 changes employed paired t- or Wilcoxon tests. Correlations (Pearson or Spearman, according to normality) were analyzed. Results were corrected at 5% FDR, with uncorrected p-values reported to highlight trends for future, larger cohorts. MRI volumes were investigated atlas-wide using FreeSurfer’s ASEG (cortical and white matter macrostructures, subcortical nuclei, ventricles and ChP, corpus callosum) and Desikan-Killiany lobar parcellations (left/right combined). FDR was applied within each anatomical group, Cohen’s d quantified effect sizes, and for nonsignificant effects we calculated the N required for 80% power. All analyses were performed in R 4.2.2. Results 1. Participants The final sample of SAUD patients were 21 males and 16 females with a mean age of 46 years. Most patients (35/37) were smokers. The median alcohol consumption was 101 units (IQR=106) of alcohol (10 g) per week. Women (M=84, SD=48) drank less alcohol than men (M=143, SD=95) before entering detoxification (t(31)=2.116, p=.042). The control group included ten women and seven men with a mean age of 43 years, only one of whom was a smoker. The two groups did not differ for age (p=.450), or gender (χ² = 0.5945, p = .440). However, the control group differed significantly from the SAUD group (at T1) in terms of lower scores for depression (W = 451, p < 0.001), anxiety (W = 410, p = 0.003), and craving measures (W = 515, p < 0.001). See Table 1. 2. Clinical data and evolution with alcohol cessation In the SAUD group, all clinical symptoms decreased from T1 to T2. Depression scores transitioned from moderate to minimal levels ( W = 411, z = -4.19, p < .001). State anxiety scores decreased from moderate to low levels ( W = 343, z = -2.27, p = .023). Total craving score also showed a statistically significant reduction (t(28)=5.921, p <.001). See Table 1. 3. Inflammation levels and evolution with alcohol cessation We first examined whether SAUD patients differed from controls at the two different time points (T1 and T2) with respect to the four plasma inflammation markers IL-8, MCP-1, MIP-1β, and TNF-α. The T1 and T2 levels in patients were both compared to the single measurements in the control group. All four cytokines were significantly higher at T1 (MCP-1: z=.014, p=.014, FDR-corrected p=.014, TNF-α: t(47) = 2.77, p = 0.008, FDR-corrected p=.014, MIP-1β: z=4.45, p<.001, FDR-corrected p<.001, IL-8: z=2.43, p=.014, FDR-corrected p=.014) in SAUD than control subjects. At T2, TNF-α (z=2.75, p = .006, FDR-corrected p=.012), and MIP-1β (t(49)=4.25, p<.001, FDR-corrected p<.001) levels remained higher in patients compared to control data. We next examined the evolution of each cytokine over time in SAUD patients. There was a statistically significant decrease in the plasma levels of MCP-1 (z=2.04, p=.041, FDR-corrected p=.056), MIP-1β (z=2.51, p=.012, FDR-corrected p=.036), and Il-8 (z=-0.29, p=.018, FDR-corrected p=.036), between T1 and T2. No such trend was observed for TNF-α, as its levels increased in half of the patients (N=15) and decreased in the other half (N=16). Given the high variability of MCP-1 levels in the population, we calculated the 95% confidence interval for their median difference 3.87 to 36.80 pg/ml. Notably, the lower end of this interval exceeds zero, thus reinforcing our finding of a significant reduction in MCP-1 levels between T1 and T2. See Figure 2. 4. Early changes in brain volume with alcohol cessation We observed a significant rise from T1 to T2 in GM volume in both cortical (t(36)=2.651, p=.012, FDR p=.020) and subcortical (t(36)=3.343, p=.002, FDR p=.007) components, as well as in cerebellar GM (t(36)=3.245, p=.003, FDR p=.007). WM volumes did not change significantly, except for an increase in cerebellar WM (t(36)=2.399, p=.022, FDR p=.027). We also noted significant volumetric decreases for the ChP (t(35)=2.097, p=.043, FDR p=.048) and the third (t(35) = 2.093, p =.044, FDR p=.048), fourth (t(35) = 3.674, p <.001), and lateral ventricles (t(35) = 2.049, p =.048, FDR p=.048). No significant changes were observed in any of the corpus callosum subdivisions. Examining specific subcortical ROIs revealed volumetric increases in the thalamus (t(36)=2.342, p=.025) and ventral diencephalon (t(36)=2.071, p=.046), though neither survived FDR correction (Supplemental Table 1). Using the Desikan‐Killiany “aparc” atlas to analyze cortical GM first by lobe, we found significant global volumetric increases for the frontal (t(36)=2.414, p=.021, FDR p=.043), parietal (t(36)=2.321, p=.026, FDR p=.043), and occipital lobes (t(36)=2.928, p=.006, FDR p=.030). There were subregional volumetric increases surviving FDR correction for multiple comparisons in the frontal pole (t(36)=2.829, p=.008, FDR p=.044), caudal middle frontal gyrus (t(36)=2.920, p=.006, FDR p=.044), pericalcarine cortex (t(36)=2.581, p=.014, FDR p=.018), cuneus (t(36)=2.693, p=.011, FDR p=.018), and lateral occipital cortex (t(36)=2.988, p=.005, FDR p=.018). (Supplemental Table 2). 5. Links between changes in the ventricles and changes in other structures Next, we tested whether ventricular volume reductions between T1 and T2 correlated with significant increases observed in other brain structures in the ASEG atlas (cortical gray matter, subcortical gray matter, cerebellar gray and white matter, the thalamus, the ventral diencephalon, and the choroid plexus). To capture overall ventricular shrinkage, we computed a composite score summarizing volume changes across the third, fourth, and lateral ventricles. We found statistically significant negative correlations between changes in ventricle volume and subcortical gray matter (r = -.448, p = .005, FDR p = .018), cerebellar gray matter (r = -.419, p = .010, FDR p = .103), and cerebellar white matter (ρ = -.364, p = .027, FDR p = .103). Among subcortical regions, the thalamus showed a negative correlation with ventricle volume (r = -.642, p < .001, FDR p < .001). In contrast, no significant correlations were found between changes in choroid plexus volume and ventricular structures. 6. Modulatory effect of demographic variables on morphometric changes and inflammation levels Additional analyses explored potential demographic influences on brain volume and cytokine changes from T1 to T2. Significant gender effects emerged for total and GM volume increases, notably in subcortical, cerebellar, occipital, and frontal regions (p-values ranging from .015 to .048), although these did not withstand FDR correction. Further paired t-tests revealed volume changes to be significant only in the men. There was no significant gender-based differences in cytokine concentrations. Additionally, neither age nor daily alcohol consumption correlated with volumetric or cytokine changes, and the high prevalence of smokers in the sample (35 out of 37) precluded analysis of smoking's impact. 7. Associations between brain volume changes and changes in clinical symptoms To investigate possible relationships between changes in clinical symptoms and brain volume from T1 to T2, we examined overall regions (main GM divisions) and then explored each cortical and subcortical subregion in more detail. No significant correlations emerged for anxiety, depression, or withdrawal severity. By contrast, changes in craving showed a negative association with changes in cortical GM volume (r = -0.386, uncorrected p = .042, FDR > .05). An exploratory, uncorrected survey across cortical regions revealed negative correlations in several frontal regions (caudal middle frontal gyrus: r = -0.41, p = .028; paracentral lobule: r = -0.40, p = .033; precentral region: r = -0.39, p = .039), the temporal lobe (inferior temporal cortex: r = -0.38, p = .046; banks of the superior temporal sulcus: r = -0.42, p = .025), and the parietal lobe (inferior parietal cortex: r = -0.39, p = .041; supramarginal gyrus: r = -0.47, p = .011; precuneus: r = -0.52, p = .004). Additionally, the volume of the posterior corpus callosum correlated negatively with changes in craving (r = -0.494, p = .008). Overall, these findings indicate that individuals who showed larger volume increases in these specific regions also experienced greater reductions in craving. 8. Associations between brain volume changes and inflammatory changes To examine a potential link between inflammation and choroid plexus (ChP) volume, we tested cytokine-ChP associations at both time points and then explored whether changes in cytokines between T1 and T2 predicted changes in ChP volume. Positive correlations emerged at T1 between ChP volume and both MCP‐1 (ρ=0.364, p=0.040, FDR p=0.080) and MIP‐1β (ρ=0.378, p=0.033, FDR p=0.080), indicating a trend toward significance after FDR correction. We then evaluated whether shifts in cytokines from T1 to T2 related to significant regional volume changes. In this exploration, MCP‐1 stood out, displaying a significant positive correlation with cortical (ρ=0.367, p=0.040, FDR p=0.094), subcortical (ρ=0.416, p=0.019, FDR p=0.076), and cerebellar GM volumes (ρ=0.355, p=0.047, FDR p=0.094), and a significant negative correlation with ventricular volume (ρ=-0.460, p=0.009, FDR p=0.072). Within the cortex, the temporal lobe showed the strongest association (ρ=0.562, p<0.001, FDR p=0.003): declining MCP‐1 coincided with shrinking GM volumes and ventricle enlargement (see Figure 3). We next made an exploratory investigation of all cortical subregions by lobe that correlated positively with the individual changes in MCP1 concentration. In the temporal lobe, there were correlations in the fusiform gyrus (ρ = 0.367, p = 0.039), inferior temporal gyrus (ρ = 0.406, p = 0.022), middle temporal gyrus (ρ = 0.387, p = 0.028) and superior temporal gyrus (ρ = 0.427, p = 0.015). In the frontal lobe, correlations were confined to parts of the inferior frontal gyrus: the pars orbitalis (ρ = 0.383, p = 0.031), the pars opercularis (ρ = 0.399, p = 0.024), and the pars triangularis (ρ = 0.462, p = 0.008) and in the medial orbitofrontal cortex (ρ = 0.564, p = 0.001). In addition, the cingulate isthmus (ρ = 0.365, p = 0.040) also showed a significant correlation. Among subcortical regions, only the volume change in thalamus correlated with the decrease in plasma MCP -1 (ρ = 0.364, p = 0.040). Moreover, the insula also exhibited a significant positive correlation with the MCP-1 reduction (ρ = 0.361, p = 0.042). 9. Associations between inflammatory changes and changes in clinical symptoms We found no link between cytokine levels and changes from T1 to T2 in depression, anxiety, or craving symptoms in the SAUD patients, nor were there any such associations in the control group. However, the SAUD patients showed a statistically significant, positive correlation between the Cushman withdrawal score and MCP-1 level at T1 (ρ=.469, uncorrected p=.009, FDR-corrected p=.036). See Figure 4. Discussion Clinical changes during withdrawal Results confirmed a positive trajectory in the psychological well-being of SAUD patients across the detoxification process, with significant reductions in the levels of anxiety, depression and craving in the first three weeks of withdrawal, aligning with previous findings (19,20,46). Cytokine dynamics and link with withdrawal symptoms At the onset of withdrawal, patients exhibited elevated plasma levels of all four cytokines, reflecting a low-grade systemic pro-inflammatory state (45,47,48). A key new observation is that baseline MCP-1 correlated with withdrawal severity, and not total alcohol intake, pointing to a specific link between inflammation and symptom intensity. MCP-1 drives monocyte recruitment, microglial activation, and increased BBB permeability ( Zhang & Luo, 2019) which may amplify systemic and central immune responses during abrupt cessation. In animal models, MCP-1 spikes by up to 1000% within 18 hours of ethanol withdrawal, coinciding with falling blood alcohol levels and supporting the concept of a withdrawal-specific neuroimmune rebound (7,51). Over 19 days of withdrawal, MCP-1 and IL-8 levels both decreased toward control levels, consistent with other studies (45,52). This reduction may reflect partial normalization of gut permeability and LPS levels, which improve within weeks of abstinence (21). Although MIP-1β also dropped, it remained higher than in controls. TNF-α showed no overall change, as it rose in half of the patients while falling in the other half, in line with previous observations (48,53). A study with longer follow-ups, as reported by Yen et al. (45), suggested that more prolonged abstinence may be necessary to arrive at stable declines in some cytokines, such as TNF-α. Individual variability could also reflect genetic or lifestyle factors (54). The fact that peripheral inflammation only partially resolves during inpatient treatment, with cytokines normalizing at varying rates, aligns with a recent review (47). Link between cytokines and the choroid plexus MCP-1 and MIP-1β levels positively correlated with choroid-plexus (ChP) volume at T1. The ChP, principal producer of CSF, acts as an immune gateway, coordinating leukocyte entry and cytokine exchange between blood and CSF (55,56). Its enlargement is a recognized marker of neuroinflammation across psychiatric disorders and likely reflects leukocyte infiltration, oedema, and cytokine-driven epithelial proliferation/remodeling (27). MCP-1 and MIP-1β, as key chemokines for monocyte recruitment (57), may exert a specific influence on ChP structure. Our data provide the first evidence of a ChP-cytokine link in SAUD and, with the subsequent ChP shrinkage observed at T2, highlight this structure as a dynamic biomarker of early neuro-immune resolution. Structural brain changes during withdrawal No significant overall white matter evolution Apart from the cerebellum, we found no significant WM changes across withdrawal, suggesting that substantial axon remodelling or remyelination does not occur within two to three weeks of abstinence. This aligns with an earlier study showing limited WM recovery at two weeks (43), but may differ from microstructural findings that revealed subtler early changes (25,58,59). Such discrepancies may reflect the limitations of T1-based volumetry in detecting localized tract‐specific remodeling, which might require longer sobriety or more sensitive imaging modalities. Notably, the cerebellum GM volume appeared to recover more rapidly, implying distinct regional plasticity in early abstinence (43). Two coexisting patterns in brain changes Our results suggest two coexisting trends in the evolution of brain recovery during abstinence. Recovery‐dominated pattern On the one hand, the group-level analysis revealed a general recovery dynamics associated with alcohol cessation, marked by an overall increase in GM volume. The increase in cerebral cortex was driven by specific regions (middle and superior frontal areas, associative parietal areas, and most of the occipital lobe), and the subcortical increase was driven by the diencephalon and the thalamus, with an additional component from the cerebellum. Previous studies have already noted widespread GM recovery within two weeks of abstinence (22,24,43,44,60,61), although the baseline intervals for those cohorts varied, which complicates a direct comparison with present results following a more tightly controlled timeline. The GM volume increases are very likely attributable to neuronal restoration processes, including rehydration (62), improved cerebral perfusion (63), dendritic regrowth (58,64), and neurotransmitter rebalancing (65). Concurrent reductions in ventricular volume, likely a direct effect of expansion of the surrounding GM (61,66), indeed correlated with the GM gains which mirrored previous findings at two (67) to four weeks (68) post‐withdrawal. The GM increase also correlated with the drop in craving in our SAUD patients, indicating a possible clinical importance of these changes. Volume increase in frontal subregions (caudal middle frontal, paracentral lobule, and precentral gyrus) is expected to reinforce executive inhibition and motor suppression of alcohol-seeking behavior (69). Temporal lobe structures (inferior temporal cortex, superior temporal sulcus) facilitate visual association and socio‐emotional processing (70), recovery of which might dampen cue‐reactivity (69). Gains in parietal areas (inferior parietal cortex, supramarginal gyrus, precuneus) mediate attentional bias, multisensory integration, and self‐referential cognition (71), which might also mediate aspects of recovery. Finally, expansion of the posterior corpus callosum likely enhances inter-hemispheric signaling, thereby improving top-down regulatory control over reward circuits (72). While grey-matter increase paralleled the decline in craving, the temporal profile of MCP-1 did not (consistent with (47) meta-analysis) suggesting that clinical improvement aligns more with structural restoration than with chemokine normalization, a dissociation future multimodal studies should probe explicitly. Inflammation‐resolution pattern On the other hand, individual-level correlations between regional brain volume decreases and MCP-1 reduction in specific regions (inferior frontal areas, temporal lobe structures, the insula, and the cingulate isthmus) suggest a second mechanism, possibly reflecting the resolution of inflammation. In the early stages of abstinence, elevated MCP-1 may contribute to microglial activation and BBB dysfunction. Although fully phagocytic amoeboid microglia are not typically observed in SAUD (73), studies report a partially activated, pro-inflammatory microglial state whether driven by chronic alcohol exposure (9,74) or by a rebound effect during withdrawal (75,76). This phenotype is sufficient to trigger cytokine release, astrocyte activation and increased BBB permeability (77). MCP-1 itself has been shown to directly increase BBB permeability (50), while astrocytic aquaporins facilitate vasogenic oedema (78), transiently expanding regional volume. Diffusion-MRI and histology further show that microglial soma hypertrophy and process retraction enlarge the extracellular space and can raise regional volume even without cell-number changes (76,79). As inflammation resolves, microglia revert to a ramified state (80) and barrier integrity is restored, possibly producing the GM contraction associated with the decrease in MCP-1. In our sample, this process unfold within two weeks, consistent with the known rapid dynamic of microglial (partial) deactivation (76,81,82) and the normalization of LPS and MCP-1 early in abstinence (21). Participants with the greatest GM deflation showed lesser shrinkage or paradoxical enlargement of ventricles, consistent with Monro-Kellie compensation. Although MCP-1 was not measured in brain tissue directly, its correlations with structural changes, choroid plexus volume, and withdrawal severity supports a model in which peripheral immune signaling, via BBB or ChP pathways, contributes to transient neuroinflammatory brain remodeling during early abstinence. The affected regions are known to host microglia with high activation potential (83) and are particularly vulnerable to inflammation across psychiatric disorders (84,85). The insula, that plays a central role in interoception and affective processing, responds to peripheral immune signals (86). These two patterns of response (post‐alcohol recovery and inflammation resolution) may cancel out at the group level, such that some changes did not emerge as significant in the T1-T2 comparison. However, we see in our population a clustering of responses (see Figure 5): “recovery‐dominant” regions showing an overall volume increase independent of MCP‐1 decline; (2) “inflammation‐modulated” regions with no significant volume change, but strong correlations to MCP‐1 reduction; and (3) “mixed‐effect” areas (thalamus, cerebellum, pars opercularis) that may exhibit a volume gain along with a “microglial deflation” component, thus tempering the net volumetric increase at T2. By demonstrating tight co-modulation of peripheral chemokines, ChP dynamics and regional grey-matter volumes, our study reinforces the need for integrated neuro-immune models of SAUD and highlight the ChP-MCP-1 axis as a promising biomarker and therapeutic lever for future interventions. Gender and SAUD severity effects Gender-related volume brain volume changes, aligned with known sex differences in alcohol metabolism, toxicity, and immunity (87,88), lost significance after FDR correction, indicating limited power. Age and pre-detoxification alcohol intake were not associated with brain or cytokine measures, suggesting that abrupt cessation, rather than cumulative exposure, drives neuro-immune observations. Study limitations and future directions First, we did not monitor smoking changes, though ongoing smoking makes it unlikely to explain our time‐dependent effects. A key strength lies is our precise control of the timing of alcohol cessation relative to measurements: all participants drank until admission or the day before, but we lacked exact final drinking episode, drinking‐day averages, and age‐at‐first‐drink data, which could moderate outcomes. A minority (N=10) showed paradoxical anxiety increases, suggesting individual trajectories that merit targeted follow‐up. Finally, MCP-1 was the most robust biomarker, but our four-cytokine panel may have missed other relevant mediators. We applied an atlas-wide, FDR-corrected analysis to keep false positives low, but we also show uncorrected trends to show effects that need larger samples. Because strict correction in a single cohort can hide biologically relevant signals (89,90), replication in an independent sample is essential, an approach now widely recommended for exploratory neuroimaging (91,92). Declarations Funding PdT received financial support for this study from the Belgian National Fund for Scientific Research (FNRS-FRS; Grant Number: 3.4585.07; http://www1.frs-fnrs.be/). Data availability The data that support the findings of this study are available from the corresponding authors upon written request. Acknowledgements We are grateful to all the participants who took part in this study for their commitment and contributions to the nursing staff at Saint-Luc Academic Hospital, Brussels, for their assistance with blood collection. We also extend our appreciation to the MRI technologists for their crucial support and expertise in imaging, and to Prof. Paul Cumming of Bern University Hospital for critical reading of the manuscript. Conflict of Interest The authors declare no competing interests. References Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. The Lancet Psychiatry. 1 août 2016;3(8):760‑73. Yang, Singla R, Maheshwari O, Fontaine CJ, Gil-Mohapel J. Alcohol use disorder: neurobiology and therapeutics. Biomedicines. 2022;10(5):1192. Crews FT, Sarkar DK, Qin L, Zou J, Boyadjieva N, Vetreno RP. Neuroimmune function and the consequences of alcohol exposure. Alcohol research: current reviews. 2015;37(2):331. Erickson EK, Grantham EK, Warden AS, Harris RA. Neuroimmune signaling in alcohol use disorder. Pharmacology Biochemistry and Behavior. 2019;177:34‑60. Ahlers KE, Karaçay B, Fuller L, Bonthius DJ, Dailey ME. Transient activation of microglia following acute alcohol exposure in developing mouse neocortex is primarily driven by BAX-dependent neurodegeneration: Microglia and Developmental Alcohol Exposure. Glia. oct 2015;63(10):1694‑713. Holloway KN, Douglas JC, Rafferty TM, Kane CJM, Drew PD. Ethanol Induces Neuroinflammation in a Chronic Plus Binge Mouse Model of Alcohol Use Disorder via TLR4 and MyD88-Dependent Signaling. Cells. janv 2023;12(16):2109. Marshall SA, Geil CR, Nixon K. Prior binge ethanol exposure potentiates the microglial response in a model of alcohol-induced neurodegeneration. Brain sciences. 2016;6(2):16. Qin L, Crews FT. NADPH oxidase and reactive oxygen species contribute to alcohol-induced microglial activation and neurodegeneration. Journal of Neuroinflammation. 2012;9:5. Crews FT, Qin L, Coleman L, Vidrascu E, Vetreno R. Cortical reactive microglia activate astrocytes, increasing neurodegeneration in human alcohol use disorder [Internet]. bioRxiv; 2025 [cité 5 juin 2025]. p. 2025.04.25.650687. Disponible sur: https://www.biorxiv.org/content/10.1101/2025.04.25.650687v1 De Carvalho LM, Wiers CE, Sun H, Wang GJ, Volkow ND. Increased transcription of TSPO, HDAC2, and HDAC6 in the amygdala of males with alcohol use disorder. Brain and Behavior. 2021;11(2):e01961. Vetreno RP, Qin L, Coleman Jr LG, Crews FT. Increased Toll-like Receptor-MyD88-NFκB-Proinflammatory neuroimmune signaling in the orbitofrontal cortex of humans with alcohol use disorder. Alcoholism: Clinical and Experimental Research. 2021;45(9):1747‑61. Hillmer AT, Sandiego CM, Hannestad J, Angarita GA, Kumar A, McGovern EM, et al. In vivo imaging of translocator protein, a marker of activated microglia, in alcohol dependence. Molecular Psychiatry. déc 2017;22(12):1759‑66. Kalk NJ, Guo Q, Owen D, Cherian R, Erritzoe D, Gilmour A, et al. Decreased hippocampal translocator protein (18 kDa) expression in alcohol dependence: a [ 11 C]PBR28 PET study. Translational Psychiatry. janv 2017;7(1):e996. Kim SW, Wiers CE, Tyler R, Shokri-Kojori E, Jang YJ, Zehra A, et al. Influence of alcoholism and cholesterol on TSPO binding in brain: PET [ 11 C]PBR28 studies in humans and rodents. Neuropsychopharmacology. août 2018;43(9):1832. Coppens V, Morrens M, Destoop M, Dom G. The interplay of inflammatory processes and cognition in alcohol use disorders—a systematic review. Frontiers in psychiatry. 2019;10:632. Raval NR, Angarita G, Matuskey D, Miller R, Drake LR, Kapinos M, et al. Imaging the brain’s immune response to alcohol with [11C]PBR28 TSPO Positron Emission Tomography. Mol Psychiatry. 2 août 2023;1‑7. Saba W, Goutal S, Auvity S, Kuhnast B, Coulon C, Kouyoumdjian V, et al. Imaging the neuroimmune response to alcohol exposure in adolescent baboons: a TSPO PET study using 18F-DPA-714. Addiction Biology. 2018;23(5):1000‑9. Li X, Ramos-Rolón AP, Kass G, Pereira-Rufino LS, Shifman N, Shi Z, et al. Imaging neuroinflammation in individuals with substance use disorders. The Journal of clinical investigation [Internet]. 2024 [cité 5 juin 2025];134(11). Disponible sur: https://www.jci.org/articles/view/172884 de Timary P, Cordovil de Sousa Uva M, Denoël C, Hebborn L, Derely M, Desseilles M, et al. The Associations between Self-Consciousness, Depressive State and Craving to Drink among Alcohol Dependent Patients Undergoing Protracted Withdrawal. PLoS ONE [Internet]. 2013;8(8). Disponible sur: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883138442&doi=10.1371%2fjournal.pone.0071560&partnerID=40&md5=1e90b7195ec586287a7ffd0fb412c213 Petit G, Luminet O, Cordovil de Sousa Uva M, Monhonval P, Leclercq S, Spilliaert Q, et al. Gender Differences in Affects and Craving in Alcohol-Dependence: A Study During Alcohol Detoxification. Alcohol Clin Exp Res. 1 janv 2017;n/a-n/a. Leclercq S, Cani PD, Neyrinck AM, Stärkel P, Jamar F, Mikolajczak M, et al. Role of intestinal permeability and inflammation in the biological and behavioral control of alcohol-dependent subjects. Brain, Behavior, and Immunity. 1 août 2012;26(6):911‑8. Bühler M, Mann K. Alcohol and the Human Brain: A Systematic Review of Different Neuroimaging Methods. Alcoholism: Clinical and Experimental Research. 2011;35(10):1771‑93. Monnig MA, Tonigan JS, Yeo RA, Thoma RJ, McCrady BS. White matter volume in alcohol use disorders: a meta‐analysis. Addiction Biology. mai 2013;18(3):581‑92. Parvaz MA, Rabin RA, Adams F, Goldstein RZ. Structural and functional brain recovery in individuals with substance use disorders during abstinence: A review of longitudinal neuroimaging studies. Drug and Alcohol Dependence. 2022;232:109319. De Santis S, Bach P, Pérez-Cervera L, Cosa-Linan A, Weil G, Vollstädt-Klein S, et al. Microstructural white matter alterations in men with alcohol use disorder and rats with excessive alcohol consumption during early abstinence. JAMA psychiatry. 2019;76(7):749‑58. Selim MK, Harel M, De Santis S, Perini I, Sommer WH, Heilig M, et al. Repetitive deep TMS in alcohol dependent patients halts progression of white matter changes in early abstinence. Psychiatry and Clinical Neurosciences. 2024;78(3):176‑85. Althubaity N, Schubert J, Martins D, Yousaf T, Nettis MA, Mondelli V, et al. Choroid plexus enlargement is associated with neuroinflammation and reduction of blood brain barrier permeability in depression. NeuroImage: Clinical. 2022;33:102926. Diez-Cirarda M, Yus-Fuertes M, Delgado-Alonso C, Gil-Martínez L, Jiménez-García C, Gil-Moreno MJ, et al. Choroid plexus volume is enlarged in long COVID and associated with cognitive and brain changes. Molecular Psychiatry. 2025;1‑10. Ricigliano VA, Stankoff B. Choroid plexuses at the interface of peripheral immunity and tissue repair in multiple sclerosis. Current Opinion in Neurology. 2023;36(3):214‑21. Zhou YF, Huang JC, Zhang P, Fan FM, Chen S, Fan HZ, et al. Choroid plexus enlargement and allostatic load in schizophrenia. Schizophrenia bulletin. 2020;46(3):722‑31. Folstein MF, Folstein SE, McHugh PR. « Mini-mental state ». A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. nov 1975;12(3):189‑98. Beck AT, Steer RA, Brown GK. Beck depression inventory-II. San Antonio, TX. 1996;78204‑2498. Spielberger CD. State-trait anxiety inventory for adults. 1983; Anton RF, Moak DH, Latham PK. The obsessive compulsive drinking scale: a new method of assessing outcome in alcoholism treatment studies. Archives of general psychiatry. 1996;53(3):225‑31. Bourque P, Beaudette D. Étude psychometrique du questionnaire de dépression de Beck auprès d’un échantillon d’étudiants universitaires francophones. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement. 1982;14(3):211. Bruchon-Schweitzer M, Paulhan I. Le manuel du STAI-Y de CD Spielberger, adaptation française. Paris: ECPA. 1993; Ansseau M, Besson J, Lejoyeux M, Pinto E, Landry U, Cornes M, et al. A French translation of the Obsessive-Compulsive Drinking Scale for craving in alcohol-dependent patients: A validation study in Belgium, France, and Switzerland. European addiction research. 2000;6(2):51‑6. Cushman Jr P, Forbes R, Lemer W, Stewart M. Alcohol Withdrawal Syndromes: Clinical Management with Lofexidine. Alcoholism: Clinical and Experimental Research. 1985;9(2):103‑8. Sobell LC, Sobell MB. Timeline Follow-Back. In: Litten RZ, Allen JP, éditeurs. Measuring Alcohol Consumption [Internet]. Totowa, NJ: Humana Press; 1992 [cité 2 oct 2023]. p. 41‑72. Disponible sur: http://link.springer.com/10.1007/978-1-4612-0357-5_3 Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage. 1999;9(2):179‑94. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain. Neuron. 31 janv 2002;33(3):341‑55. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically Parcellating the Human Cerebral Cortex. Cereb Cortex. 2004;14(1):11‑22. van Eijk J, Demirakca T, Frischknecht U, Hermann D, Mann K, Ende G. Rapid Partial Regeneration of Brain Volume During the First 14 Days of Abstinence from Alcohol. Alcohol Clin Exp Res. 1 janv 2013;37(1):67‑74. Kühn S, Charlet K, Schubert F, Kiefer F, Zimmermann P, Heinz A, et al. Plasticity of Hippocampal Subfield Volume Cornu Ammonis 2+3 Over the Course of Withdrawal in Patients With Alcohol Dependence. JAMA Psychiatry. 1 juill 2014;71(7):806‑11. Yen CH, Ho PS, Yeh YW, Liang CS, Kuo SC, Huang CC, et al. Differential cytokine levels between early withdrawal and remission states in patients with alcohol dependence. Psychoneuroendocrinology. 1 févr 2017;76:183‑91. de Timary P, Luts A, Hers D, Luminet O. Absolute and relative stability of alexithymia in alcoholic inpatients undergoing alcohol withdrawal: Relationship to depression and anxiety. Psychiatry Research. 15 janv 2008;157(1):105‑13. Adams C, Conigrave JH, Lewohl J, Haber P, Morley KC. Alcohol use disorder and circulating cytokines: A systematic review and meta-analysis. Brain, Behavior, and Immunity. 1 oct 2020;89:501‑12. Leclercq S, Cani PD, Neyrinck AM, Stärkel P, Jamar F, Mikolajczak M, et al. Role of intestinal permeability and inflammation in the biological and behavioral control of alcohol-dependent subjects. Brain, behavior, and immunity. 2012;26(6):911‑8. Deshmane SL, Kremlev S, Amini S, Sawaya BE. Monocyte Chemoattractant Protein-1 (MCP-1): An Overview. Journal of Interferon & Cytokine Research. juin 2009;29(6):313‑26. Zhang K, Luo J. Role of MCP-1 and CCR2 in alcohol neurotoxicity. Pharmacological research. 2019;139:360‑6. Walter TJ, Crews FT. Microglial depletion alters the brain neuroimmune response to acute binge ethanol withdrawal. J Neuroinflammation. déc 2017;14(1):86. Girard M, Malauzat D, Nubukpo P. Serum inflammatory molecules and markers of neuronal damage in alcohol-dependent subjects after withdrawal. The World Journal of Biological Psychiatry. 2 janv 2019;20(1):76‑90. Li W, Amet T, Xing Y, Yang D, Liangpunsakul S, Puri P, et al. Alcohol abstinence ameliorates the dysregulated immune profiles in patients with alcoholic hepatitis: A prospective observational study. Hepatology. 2017;66(2):575‑90. D’Esposito V, Di Tolla MF, Lecce M, Cavalli F, Libutti M, Misso S, et al. Lifestyle and Dietary Habits Affect Plasma Levels of Specific Cytokines in Healthy Subjects. Front Nutr [Internet]. 24 juin 2022 [cité 5 déc 2024];9. Disponible sur: https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2022.913176/full Damkier HH, Brown PD, Praetorius J. Cerebrospinal Fluid Secretion by the Choroid Plexus. Physiological Reviews. oct 2013;93(4):1847‑92. Schwartz M, Baruch K. The resolution of neuroinflammation in neurodegeneration: leukocyte recruitment via the choroid plexus. The EMBO Journal. 2 janv 2014;33(1):7‑22. Kabashima H, Yoneda M, Nagata K, Hirofuji T, Maeda K. THE PRESENCE OF CHEMOKINE (MCP-1, MIP-1α, MIP-1β, IP-10, RANTES)-POSITIVE CELLS AND CHEMOKINE RECEPTOR (CCR5, CXCR3)-POSITIVE CELLS IN INFLAMED HUMAN GINGIVAL TISSUES. Cytokine. 1 oct 2002;20(2):70‑7. Santis SD, Cosa-Linan A, Garcia-Hernandez R, Dmytrenko L, Vargova L, Vorisek I, et al. Chronic alcohol consumption alters extracellular space geometry and transmitter diffusion in the brain. Science Advances. 1 juin 2020;6(26):eaba0154. Selim MK, Harel M, De Santis S, Perini I, Sommer WH, Heilig M, et al. Repetitive deep TMS in alcohol dependent patients halts progression of white matter changes in early abstinence. Psychiatry and Clinical Neurosciences. 2024;78(3):176‑85. Bach P, Koopmann A, Bumb JM, Vollstädt-Klein S, Reinhard I, Rietschel M, et al. Leptin predicts cortical and subcortical gray matter volume recovery in alcohol dependent patients: A longitudinal structural magnetic resonance imaging study. Hormones and behavior. 2020;124:104749. Pfefferbaum A, Sullivan EV, Mathalon DH, Shear PK, Rosenbloom MJ, Lim KO. Longitudinal Changes in Magnetic Resonance Imaging Brain Volumes in Abstinent and Relapsed Alcoholics. Alcoholism: Clinical and Experimental Research. 1995;19(5):1177‑91. Schweinsburg BC, Taylor MJ, Videen JS, Alhassoon OM, Patterson TL, Grant I. Elevated myo ‐Inositol in Gray Matter of Recently Detoxified but Not Long‐Term Abstinent Alcoholics: A Preliminary MR Spectroscopy Study. Alcoholism Clin & Exp Res. mai 2000;24(5):699‑705. Ende G, Welzel H, Walter S, Weber-Fahr W, Diehl A, Hermann D, et al. Monitoring the effects of chronic alcohol consumption and abstinence on brain metabolism: a longitudinal proton magnetic resonance spectroscopy study. Biological psychiatry. 2005;58(12):974‑80. Bartsch AJ, Homola G, Biller A, Smith SM, Weijers HG, Wiesbeck GA, et al. Manifestations of early brain recovery associated with abstinence from alcoholism. Brain. 2007;130(1):36‑47. Köhler S, Klimke S, Hellweg R, Lang UE. Serum brain-derived neurotrophic factor and nerve growth factor concentrations change after alcohol withdrawal: preliminary data of a case-control comparison. European Addiction Research. 2013;19(2):98‑104. Zahr NM, Mayer D, Rohlfing T, Hasak MP, Hsu O, Vinco S, et al. Brain Injury and Recovery Following Binge Ethanol: Evidence from In Vivo Magnetic Resonance Spectroscopy. Biological Psychiatry. 1 mai 2010;67(9):846‑54. van Eijk J, Demirakca T, Frischknecht U, Hermann D, Mann K, Ende G. Rapid Partial Regeneration of Brain Volume During the First 14 Days of Abstinence from Alcohol. Alcohol: Clinical and Experimental Research. 2013;37(1):67‑74. Gazdzinski S, Durazzo TC, Meyerhoff DJ. Temporal dynamics and determinants of whole brain tissue volume changes during recovery from alcohol dependence. Drug and Alcohol Dependence. 1 juin 2005;78(3):263‑73. Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. août 2016;3(8):760‑73. Deen B, Koldewyn K, Kanwisher N, Saxe R. Functional organization of social perception and cognition in the superior temporal sulcus. Cerebral cortex. 2015;25(11):4596‑609. Lusher J, Chandler C, Ball D. Alcohol dependence and the alcohol Stroop paradigm: evidence and issues. Drug and Alcohol Dependence. 6 sept 2004;75(3):225‑31. Gaudreault PO, King SG, Malaker P, Alia-Klein N, Goldstein RZ. Whole-brain white matter abnormalities in human cocaine and heroin use disorders: association with craving, recency, and cumulative use. Mol Psychiatry. févr 2023;28(2):780‑91. Marshall SA, McClain JA, Kelso ML, Hopkins DM, Pauly JR, Nixon K. Microglial activation is not equivalent to neuroinflammation in alcohol-induced neurodegeneration: The importance of microglia phenotype. Neurobiology of disease. 2013;54:239‑51. Cruz C, Meireles M, Silva SM. Chronic ethanol intake induces partial microglial activation that is not reversed by long-term ethanol withdrawal in the rat hippocampal formation. NeuroToxicology. 1 mai 2017;60:107‑15. Crews FT, Macht V, Vetreno RP. Epigenetic regulation of microglia and neurons by proinflammatory signaling following adolescent intermittent ethanol (AIE) exposure and in human AUD. Advances in Drug and Alcohol Research. 2024;4:12094. De Santis S, Selim MK, Canals S. Brain Microstructure in Alcohol Addiction: Characterization of Diffusion-Based MRI Biomarkers, Neuropathological Substrates, and Functional Consequences. In: Mueller S, Heilig M, éditeurs. Alcohol and Alcohol-related Diseases [Internet]. Cham: Springer International Publishing; 2023 [cité 4 janv 2024]. p. 493‑508. Disponible sur: https://doi.org/10.1007/978-3-031-32483-3_27 Huang X, Hussain B, Chang J. Peripheral inflammation and blood–brain barrier disruption: effects and mechanisms. CNS Neuroscience & Therapeutics. 2021;27(1):36‑47. Stokum JA, Kurland DB, Gerzanich V, Simard JM. Mechanisms of Astrocyte-Mediated Cerebral Edema. Neurochem Res. févr 2015;40(2):317‑28. Gursky ZH, Johansson JR, Klintsova AY. Postnatal alcohol exposure and adolescent exercise have opposite effects on cerebellar microglia in rat. Intl J of Devlp Neuroscience. oct 2020;80(6):558‑71. Gotkiewicz M, Capra J, Miettinen PO, Natunen T, Tanila H. Three-dimensional view of microglia—amyloid plaque interactions. Glia. 2025;73(1):196‑209. Sanchez-Alavez M, Nguyen W, Mori S, Wills DN, Otero D, Ehlers CL, et al. Time course of microglia activation and brain and blood cytokine/chemokine levels following chronic ethanol exposure and protracted withdrawal in rats. Alcohol. 2019;76:37‑45. Zhao YN, Wang F, Fan YX, Ping GF, Yang JY, Wu CF. Activated microglia are implicated in cognitive deficits, neuronal death, and successful recovery following intermittent ethanol exposure. Behavioural Brain Research. 1 janv 2013;236:270‑82. Tan YL, Yuan Y, Tian L. Microglial regional heterogeneity and its role in the brain. Mol Psychiatry. févr 2020;25(2):351‑67. Bond DJ, Andreazza AC, Torres IJ, Honer WG, Lam RW, Yatham LN. Association of total peripheral inflammation with lower frontal and temporal lobe volumes in early-stage bipolar disorder: A proof-of-concept study. Journal of Affective Disorders. 15 déc 2022;319:229‑34. Radewicz K, Garey LJ, Gentleman SM, Reynolds R. Increase in HLA-DR Immunoreactive Microglia in Frontal and Temporal Cortex of Chronic Schizophrenics. Journal of Neuropathology & Experimental Neurology. 1 févr 2000;59(2):137‑50. Kerezoudis P, Howe CL, Wu LJ, Lundstrom BN, Van Gompel JJ. Insula and the Immune System: More than mere Co-existence? Neurosci Bull. 1 oct 2022;38(10):1271‑3. Maynard ME, Barton EA, Robinson CR, Wooden JI, Leasure JL. Sex differences in hippocampal damage, cognitive impairment, and trophic factor expression in an animal model of an alcohol use disorder. Brain Struct Funct. janv 2018;223(1):195‑210. Pascual M, Montesinos J, Marcos M, Torres J, Costa‐Alba P, García‐García F, et al. Gender differences in the inflammatory cytokine and chemokine profiles induced by binge ethanol drinking in adolescence. Addiction Biology. nov 2017;22(6):1829‑41. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. mai 2013;14(5):365‑76. Ioannidis JPA. Why Most Published Research Findings Are False. PLOS Medicine. 30 août 2005;2(8):e124. Carp J. On the plurality of (methodological) worlds: estimating the analytic flexibility of FMRI experiments. Frontiers in neuroscience. 2012;6:149. Poldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, Munafò MR, et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci. févr 2017;18(2):115‑26. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryTable1.docx Supplementary Table 1 SupplementaryTable2.docx Supplementary Table 2 GraphicalAbstract.docx Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Translational Psychiatry → Version 1 posted Editorial decision: revise 29 Jul, 2025 Review # 2 received at journal 27 Jul, 2025 Review # 3 received at journal 24 Jul, 2025 Review # 1 received at journal 11 Jul, 2025 Reviewer # 3 agreed at journal 11 Jul, 2025 Reviewer # 2 agreed at journal 08 Jul, 2025 Reviewer # 1 agreed at journal 02 Jul, 2025 Reviewers invited by journal 01 Jul, 2025 Editor assigned by journal 19 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 18 Jun, 2025 Unknown event 18 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6915490","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":473583372,"identity":"1f219c34-4231-47ee-b2a5-73a4581cbc90","order_by":0,"name":"Geraldine Petit","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9663-2883","institution":"Université catholique de Louvain","correspondingAuthor":true,"prefix":"","firstName":"Geraldine","middleName":"","lastName":"Petit","suffix":""},{"id":473583373,"identity":"9b400f00-d50a-4e6b-9d21-308ab8431557","order_by":1,"name":"Selim Mohamed Kotb","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Selim","middleName":"Mohamed","lastName":"Kotb","suffix":""},{"id":473583374,"identity":"0be00f92-288a-46a4-9268-1c4ee858e6d0","order_by":2,"name":"Santiago Canals","email":"","orcid":"https://orcid.org/0000-0003-2175-8139","institution":"Instituto de Neurociencias (Universidad Miguel Hernández - Consejo Superior de Investigaciones Científicas)","correspondingAuthor":false,"prefix":"","firstName":"Santiago","middleName":"","lastName":"Canals","suffix":""},{"id":473583375,"identity":"2e36ffcf-07ce-4ffd-b236-2f50a1edb1a3","order_by":3,"name":"Peter Starkel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Starkel","suffix":""},{"id":473583376,"identity":"79d120f7-8680-4947-82aa-85869f05b675","order_by":4,"name":"Laurence Dricot","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Laurence","middleName":"","lastName":"Dricot","suffix":""},{"id":473583377,"identity":"d1cf2ece-6315-43c9-b9a7-f8412a8222d8","order_by":5,"name":"Marie Poncin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Marie","middleName":"","lastName":"Poncin","suffix":""},{"id":473583378,"identity":"80f1a88c-8aa6-40ad-8acd-365ac8fc17e4","order_by":6,"name":"Sophie Leclercq","email":"","orcid":"https://orcid.org/0000-0002-2894-5220","institution":"Catholic University of Louvain","correspondingAuthor":false,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Leclercq","suffix":""},{"id":473583379,"identity":"13f2fb6e-6bb3-4754-a2d5-80993d1688e0","order_by":7,"name":"Philippe de Timary","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Philippe","middleName":"","lastName":"de Timary","suffix":""}],"badges":[],"createdAt":"2025-06-17 14:41:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6915490/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6915490/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41398-026-03907-9","type":"published","date":"2026-02-28T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85187962,"identity":"959aacfe-bf6e-48cc-9696-084840a10139","added_by":"auto","created_at":"2025-06-23 08:24:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67236,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy protocol for alcohol detoxification and assessment\u003c/strong\u003e. Timeline illustrating the study design across the three weeks of alcohol detoxification: T1 (Day 1/Day 2) at the start and T2 (Day 19) at discharge. Clinical evaluations, MRI imaging, and blood samples were collected at both time points to monitor changes in systemic inflammation, brain morphometry, and clinical symptoms.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/7e50de0667614209e8fe7da7.png"},{"id":85188386,"identity":"921fa5b1-8b1d-4ce0-b22c-2ea1e9a975b4","added_by":"auto","created_at":"2025-06-23 08:32:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84774,"visible":true,"origin":"","legend":"\u003cp\u003eIn the first column, circulating cytokines levels of the patient group at T1 and T2 compared to the control group. In the second column, evolution of cytokine levels between T1 and T2 in SAUD patients.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/35a49836efb393f1dc983de2.png"},{"id":85187950,"identity":"60c7113b-575f-476b-b8dd-5a08256b1938","added_by":"auto","created_at":"2025-06-23 08:24:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110754,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between changes in MCP-1 concentrations between T1 and T2 and changes in brain regions volume.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/72bd74eb7033d4cdf03e2872.png"},{"id":85187930,"identity":"f957f188-9d9f-4d23-8894-a11f1c69bc16","added_by":"auto","created_at":"2025-06-23 08:24:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62706,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between Cushman score at T1 and MCP-1 concentrations at T1.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/4a2dad02b2a1dcea546dc0f4.png"},{"id":85187945,"identity":"f9896d3a-8c99-4208-9188-3f1fce08b4f2","added_by":"auto","created_at":"2025-06-23 08:24:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":220617,"visible":true,"origin":"","legend":"\u003cp\u003eColor-coded map of cortical and subcortical regions, illustrating different patterns of volumetric change after three weeks of abstinence in SAUD patients. Green indicates a significant volume increase between T1 and T2. Red denotes regions in which volume change correlated with declining plasma MCP-1 level. Purple highlights areas that show regions with concomitant group-level volume increase and a correlation with MCP-1 decline. Black marks regions with no significant change.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/9384dfe7e909565f9d3c0269.png"},{"id":105039460,"identity":"a57b36bb-04a5-4487-bc09-e81ea336ff25","added_by":"auto","created_at":"2026-03-20 07:46:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1440725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/fc5a0abd-5431-4c9b-9251-b15336f7a8fe.pdf"},{"id":85187966,"identity":"ba2231c5-2f8d-4ab2-b8f5-443019a74af9","added_by":"auto","created_at":"2025-06-23 08:24:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18985,"visible":true,"origin":"","legend":"Supplementary Table 1","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/232e70b555923fc5179e78bf.docx"},{"id":85187931,"identity":"1c1c4303-f68f-4ea7-86b5-73b6eed873bf","added_by":"auto","created_at":"2025-06-23 08:24:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19722,"visible":true,"origin":"","legend":"Supplementary Table 2","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/9975fab308fb5cef903707ce.docx"},{"id":85187942,"identity":"5608407b-a7d3-4c05-a0d9-3929bed7448a","added_by":"auto","created_at":"2025-06-23 08:24:34","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":112954,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-6915490/v1/1103dc84a960b42245891c7e.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Early abstinence in severe alcohol use disorder: MCP-1 decline, choroid plexus shrinkage, and region-specific grey-matter volume changes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSevere alcohol use disorder (SAUD) is a major cause of preventable disability and mortality. Beyond its effects on brain structure and neurotransmission (Koob \u0026amp; Volkow, 2016a), increasing evidence implicates neuro-inflammation in alcohol-related brain damage (Erickson et al., 2019). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn SAUD, inflammation encompasses systemic immune activation (e.g., elevated TNF-α, IL-6 due to gut-derived endotoxins), neuroinflammation (glial activation in the CNS), and microgliosis (a more specific microglial response involving morphological and functional shifts). These processes, often co-occurring, may interact through gut-brain and blood-brain barrier (BBB) dysfunction to exacerbate central nervous system (CNS) injury (4).\u003c/p\u003e\n\u003cp\u003eAnimal studies show that chronic alcohol exposure activates microglia, upregulates TLRs, and increases proinflammatory cytokines (e.g., IL-1β, TNF-α, MCP-1) in the hippocampus and prefrontal cortex, contributing to structural and cognitive impairments (Ahlers et al., 2015; Holloway et al., 2023). Human post-mortem studies corroborate these findings, with increased microglial markers and proinflammatory signaling in the cingulate cortex, amygdala, VTA, and orbitofrontal cortex of individuals with SAUD (9–11). Notably, Crews et al. (9) identified a phagocytic-like microglial phenotype in the orbitofrontal cortex (upregulated Iba1, CD11b, CCR2, and reduced Tmem119, SOCS3) and showed that microglial blockade in alcohol-exposed mice blunted astrocyte reactivity and oxidative stress, evidence that neuroimmune activation drives alcohol-related neurotoxicity.\u003c/p\u003e\n\u003cp\u003eDespite this compelling evidence from preclinical and post-mortem research, neuroimaging findings remain inconsistent. Studies using positron emission tomography (PET) with the microglial translocator protein (TSPO) radioligand \u0026nbsp;\u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/20641745/\"\u003eN-(2-[\u003csup\u003e11\u003c/sup\u003eC]methoxybenzyl)-2-phenoxy-5-pyridinamine\u0026nbsp;\u003c/a\u003e([\u003csup\u003e11\u003c/sup\u003eC]PBR28) report inconsistent results, with lower or unchanged TSPO binding in detoxified SAUD patients (12–14). This may reflect TSPO downregulation or microglial loss/dysfunction (“burn-out”) after chronic activation, leading to impaired neuroprotection and cognitive deficits (Hillmer et al., 2017). Conversely, acute alcohol exposure transiently increases TSPO binding in humans (16) and non-human primates (17), suggesting that neuroimmune responses to alcohol may be dynamic and time-sensitive and difficult to capture by single time-point scans. These findings underscore the need\u0026nbsp;for longitudinal, multimodal designs that combine neuroimaging with peripheral biomarkers to track immune dynamics across intoxication and recovery\u0026nbsp;(18).\u003c/p\u003e\n\u003cp\u003eTo address this, we conducted a longitudinal, test-retest study in SAUD patients over three weeks of abstinence, designed to track within-subject changes in peripheral inflammation (via circulating cytokines), brain structure (via MRI), and clinical symptoms (depression, anxiety, craving, and alcohol withdrawal severity). This early abstinence window is characterized by rapid clinical improvement (19,20) and partial normalization of peripheral cytokines (21), yet brain inflammatory processes in early abstinence are still poorly described (Parvaz et al., 2022), in part due to limitations in study design or timing. Recent MRI studies have shown evolving gray and white matter changes within the first weeks of abstinence (25,26), changes that resemble microglial activation patterns observed in animal models. However, direct evidence linking these changes to systemic inflammation or clinical outcomes in SAUD remains scarce.\u003c/p\u003e\n\u003cp\u003eWe also focused on the choroid plexuses (ChP), increasingly recognized as key neuroimmune interfaces at the blood-CSF barrier (27).\u0026nbsp;The ChP act as immune sensors and modulators, and show structural and functional changes in other inflammatory disorders such as multiple sclerosis, Alzheimer, schizophrenia and major depression\u0026nbsp;(27–30).\u0026nbsp;Despite their central immunological role, they remain unexplored in the context of SAUD.\u003c/p\u003e\n\u003cp\u003eTo maximize interpretability, we tightly controlled the timing of MRI and cytokine assessments relative to last alcohol and food intake. This precise synchronization enabled us to better capture short-term neuroimmune dynamics and their potential role in early recovery from alcohol dependence.\u003c/p\u003e"},{"header":"Methods ","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eParticipants\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe recruited SAUD patients admitted to Saint-Luc Academic Hospital (Brussels, Belgium) for a three-week detoxification program from 2015 to 2019. Diagnosis was established by psychiatric interview (DSM-5), and patients had been drinking until the day of admission or the day before. A control group matched for age and gender was available for comparison of inflammatory markers.\u003c/p\u003e\n\u003cp\u003eExclusion criteria included chronic inflammatory bowel disease, rheumatoid arthritis or other chronic inflammatory conditions, cancer, obesity (BMI\u0026gt;30 kg/m²), diabetes, and bariatric surgery. Patients were also excluded if they had taken antibiotics, probiotics, or prebiotics within two months, or anti-inflammatory drugs (NSAIDs/glucocorticoids) within one month before inclusion. Additional exclusions were cirrhosis, significant liver fibrosis (≥F2 on Fibroscan), any other Axis I DSM-5 disorder or major cognitive impairment (i.e., a score of ≤25 on the MMSE; (31)), use of drugs other than alcohol/nicotine, or contraindications to MRI (pregnancy, pacemaker/metal implants, claustrophobia). All patients received diazepam (40-60 mg/day) at the start of detoxification, with progressive tapering.\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from each participant after a full explanation of the study’s objectives, procedures, and possible risks. The study protocol was approved by the “Comité d’éthique Hospitalo-facultaire Saint-Luc UCLouvain” (2014/31dec/614).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eProcedure\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon admission on Day 1, patients consenting to participate underwent an MRI scan (strictly between 6 and 8 hours after admission for all patients) and received questionnaires to evaluate their clinical symptoms. Overnight fasting blood samples were taken from all participants on Day 2 between 8:00 and 8:30 AM. These tests together constituted the evaluation at time 1 (T1). At the end of the detoxification program (Day 19), patients were re-evaluated by MRI scanning, questionnaires for clinical symptoms, and blood sample, together constituting T2 (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eClinical symptoms questionnaires\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSymptoms of depression, anxiety, and alcohol craving were measured using three self-report questionnaires: the Beck Depression Inventory (BDI) (32), the State-Trait Anxiety Inventory (STAI Form YA) (33), and the Obsessive-Compulsive Drinking Scale (OCDS) (34). The BDI, used in its French BDI-II version (35) comprises 21 items (maximum score 63; cutoffs: 0-11 minimal, 12-19 mild, 20-27 moderate, 28-63 severe) assessing depressive symptoms over the previous two weeks. The STAI Form YA’s state subscale, which contains 20 items scoring current state anxiety (range 20-80; 20-39 low, 40-59 moderate, 60-80 high), was used in its validated French version (36). The OCDS (14 items) captures cognitive aspects of craving in the preceding seven days; we employed a French translation \u0026nbsp;(37), omitting items on current alcohol consumption, since participants were under supervised detoxification.\u003c/p\u003e\n\u003cp\u003eWithdrawal intensity scores were measured by nurses using the Cushman Scale (38),\u0026nbsp;which evaluates heart rate, systolic blood pressure, respiration, tremor, perspiration, agitation, and sensory disturbances (each scored 0-3, total possible 18). These ratings were conducted three times on Day 2 at six-hour intervals, with averaging to give the overall withdrawal severity. Pre-hospitalization alcohol consumption (grams/day) over the previous 30 days was quantified using the timeline follow-back method (39).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eBiological sample collection\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood was collected in EDTA tubes and centrifuged at 1000 g for 15 minutes at 4 °C. Plasma was stored at −80 °C until the time of analysis. Plasma levels of four inflammatory cytokines (interleukin 8 (IL-8), monocyte chemoattractant protein 1 (MCP-1), macrophage inflammatory protein 1 beta (MIP-1β), and tumor necrosis factor alpha (TNF-α)) were measured by a multiplex cytokine assay (Human Bio-Plex; Bio-Rad Laboratories Inc., Hercules, CA, USA) according to instructions from the manufacturer. The selection of these four pro-inflammatory cytokines was based on reliable detection limits within our samples.\u0026nbsp;The detection thresholds were as follows: IL-8 (0.4 pg/mL), MCP-1 (1.9 pg/mL), MIP-1β (3.0 pg/mL), and TNF-α (0.7 pg/mL). Plasma levels of other kit analytes fell below their respective detection levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eMRI acquisition\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree-dimensional (3D) anatomical images with heavy T1-weighting were obtained using a 3T Achieva scanner from Philips Healthcare, employing a 32-channel phased array head coil. Patients were instructed to stay still and were securely positioned in the head-holder coil, with the provision of soft earplugs for comfort. The 3D sequence involved a gradient echo sequence with an inversion prepulse (Turbo Field Echo) captured in the sagittal plane, with parameters as follows: TR/TE/flip angle = 9.1 ms/4.6 ms/8°, 150 slices, slice thickness = 1 mm, FOV = 220 × 197 mm², acquisition matrix = 296 × 247 (refined to 320*320), in-plane resolution = 0.81 × 0.95 mm² (acquisition) refined to 0.75 × 0.75 mm\u003csup\u003e2\u003c/sup\u003e, and a SENSE factor = 1.5 (for parallel imaging).\u003c/p\u003e\n\u003cp\u003eThe whole brain was analyzed and volumetrically segmented in the FreeSurfer image analysis suite (version 6) (http://surfer.nmr.mgh.harvard.edu/), using a series of automated steps. This final volumetric segmentation amalgamates data derived from a universal probabilistic atlas and subject-specific measured values, allowing for detailed volumetric analysis. The reference atlas derives from \u0026nbsp;a training set of \u0026nbsp;40 healthy young subjects, whose brain structures were manually labeled (40–42).\u003c/p\u003e\n\u003cp\u003eAfter careful visual examination of each completed segmentation any detected inaccuracies in geometry were corrected before proceeding in the data analysis pipeline. The only relevant inaccuracies were minor discrepancies related to skull stripping.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eStatistical analyses\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe enrolled all eligible SAUD patients during the recruitment period (n = 37), in line with prior studies (n ≈ 25-40) that detected significant MRI and cytokine changes in early abstinence (21,43–45). Between‐group (T1 vs. controls) comparisons of demographics, clinical scores and cytokines used two‐sided t-tests (Levene’s/Welch’s or nonparametric as needed), and within‐patient T1-T2 changes employed paired t- or Wilcoxon tests. Correlations (Pearson or Spearman, according to normality) were analyzed. Results were corrected at 5% FDR, with uncorrected p-values reported to highlight trends for future, larger cohorts. MRI volumes were investigated atlas-wide using FreeSurfer’s ASEG (cortical and white matter macrostructures, subcortical nuclei, ventricles and ChP, corpus callosum) and Desikan-Killiany lobar parcellations (left/right combined). FDR was applied within each anatomical group, Cohen’s d quantified effect sizes, and for nonsignificant effects we calculated the N required for 80% power. All analyses were performed in R 4.2.2.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eParticipants\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final sample of SAUD patients were 21 males and 16 females with a mean age of 46 years. Most patients (35/37) were smokers. The median alcohol consumption was 101 units (IQR=106) of alcohol (10 g) per week. Women (M=84, SD=48) drank less alcohol than men (M=143, SD=95) before entering detoxification (t(31)=2.116, p=.042). The control group included ten women and seven men with a mean age of 43 years, only one of whom was a smoker. The two groups did not differ for age (p=.450), or gender (χ² = 0.5945, p = .440). However, the control group differed significantly from the SAUD group (at T1) in terms of lower scores for depression (W = 451, p \u0026lt; 0.001), anxiety (W = 410, p = 0.003), and craving measures (W = 515, p \u0026lt; 0.001). See Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eClinical data and evolution with alcohol cessation\u003c/u\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the SAUD group, all clinical symptoms decreased from T1 to T2. Depression scores transitioned from moderate to minimal levels (\u003cem\u003eW\u003c/em\u003e = 411, \u003cem\u003ez\u003c/em\u003e = -4.19, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). State anxiety scores decreased from moderate to low levels (\u003cem\u003eW\u003c/em\u003e = 343, \u003cem\u003ez\u003c/em\u003e = -2.27, \u003cem\u003ep\u003c/em\u003e = .023). Total craving score also showed a statistically significant reduction (t(28)=5.921, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001). See Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eInflammation levels and evolution with alcohol cessation\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first examined whether SAUD patients differed from controls at the two different time points (T1 and T2) with respect to the four plasma inflammation markers IL-8, MCP-1, MIP-1β, and TNF-α. The T1 and T2 levels in patients were both compared to the single measurements in the control group.\u003c/p\u003e\n\u003cp\u003eAll four cytokines were significantly higher at T1 (MCP-1: z=.014, p=.014, FDR-corrected p=.014, TNF-α: t(47) = 2.77, p = 0.008, FDR-corrected p=.014, MIP-1β: z=4.45, p\u0026lt;.001, FDR-corrected p\u0026lt;.001, IL-8: z=2.43, p=.014, FDR-corrected p=.014) in SAUD than control subjects. At T2, TNF-α (z=2.75, p = .006, FDR-corrected p=.012), and MIP-1β (t(49)=4.25, p\u0026lt;.001, FDR-corrected p\u0026lt;.001) levels remained higher in patients compared to control data.\u003c/p\u003e\n\u003cp\u003eWe next examined the evolution of each cytokine over time in SAUD patients. There was a statistically significant decrease in the plasma levels of MCP-1 (z=2.04, p=.041, FDR-corrected p=.056), MIP-1β (z=2.51, p=.012, FDR-corrected p=.036), and Il-8 (z=-0.29, p=.018, FDR-corrected p=.036), between T1 and T2. No such trend was observed for TNF-α, as its levels increased in half of the patients (N=15) and decreased in the other half (N=16). Given the high variability of MCP-1 levels in the population, we calculated the 95% confidence interval for their median difference 3.87 to 36.80 pg/ml. Notably, the lower end of this interval exceeds zero, thus reinforcing our finding of a significant reduction in MCP-1 levels between T1 and T2. See Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eEarly changes in brain volume with alcohol cessation\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe observed a significant rise from T1 to T2 in GM volume in both cortical (t(36)=2.651, p=.012, FDR p=.020) and subcortical (t(36)=3.343, p=.002, FDR p=.007) components, as well as in cerebellar GM (t(36)=3.245, p=.003, FDR p=.007). WM volumes did not change significantly, except for an increase in cerebellar WM (t(36)=2.399, p=.022, FDR p=.027).\u003c/p\u003e\n\u003cp\u003eWe also noted significant volumetric decreases for the ChP (t(35)=2.097, p=.043, FDR p=.048) and the third (t(35) = 2.093, p =.044, FDR p=.048), fourth (t(35) = 3.674, p \u0026lt;.001), and lateral ventricles (t(35) = 2.049, p =.048, FDR p=.048). No significant changes were observed in any of the corpus callosum subdivisions.\u003c/p\u003e\n\u003cp\u003eExamining specific subcortical ROIs revealed volumetric increases in the thalamus (t(36)=2.342, p=.025) and ventral diencephalon (t(36)=2.071, p=.046), though neither survived FDR correction (Supplemental Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing the Desikan‐Killiany “aparc” atlas to analyze cortical GM first by lobe, we found significant global volumetric increases for the frontal (t(36)=2.414, p=.021, FDR p=.043), parietal (t(36)=2.321, p=.026, FDR p=.043), and occipital lobes (t(36)=2.928, p=.006, FDR p=.030). There were subregional volumetric increases surviving FDR correction for multiple comparisons in the frontal pole (t(36)=2.829, p=.008, FDR p=.044), caudal middle frontal gyrus (t(36)=2.920, p=.006, FDR p=.044), pericalcarine cortex (t(36)=2.581, p=.014, FDR p=.018), cuneus (t(36)=2.693, p=.011, FDR p=.018), and lateral occipital cortex (t(36)=2.988, p=.005, FDR p=.018). (Supplemental Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eLinks between changes in the ventricles and changes in other structures\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we tested whether ventricular volume reductions between T1 and T2 correlated with significant increases observed in other brain structures in the ASEG atlas (cortical gray matter, subcortical gray matter, cerebellar gray and white matter, the thalamus, the ventral diencephalon, and the choroid plexus). To capture overall ventricular shrinkage, we computed a composite score summarizing volume changes across the third, fourth, and lateral ventricles. We found statistically significant negative correlations between changes in ventricle volume and subcortical gray matter (r = -.448, p = .005, FDR p = .018), cerebellar gray matter (r = -.419, p = .010, FDR p = .103), and cerebellar white matter (ρ = -.364, p = .027, FDR p = .103). Among subcortical regions, the thalamus showed a negative correlation with ventricle volume (r = -.642, p \u0026lt; .001, FDR p \u0026lt; .001).\u0026nbsp;In contrast, no significant correlations were found between changes in choroid plexus volume and ventricular structures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eModulatory effect of demographic variables on morphometric changes and inflammation levels\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional analyses explored potential demographic influences on brain volume and cytokine changes from T1 to T2. Significant gender effects emerged for total and GM volume increases, notably in subcortical, cerebellar, occipital, and frontal regions (p-values ranging from .015 to .048), although these did not withstand FDR correction. Further paired t-tests revealed volume changes to be significant only in the men. There was no significant gender-based differences in cytokine concentrations. Additionally, neither age nor daily alcohol consumption correlated with volumetric or cytokine changes, and the high prevalence of smokers in the sample (35 out of 37) precluded analysis of smoking's impact.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eAssociations between brain volume changes and changes in clinical symptoms\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate possible relationships between changes in clinical symptoms and brain volume from T1 to T2, we examined overall regions (main GM divisions) and then explored each cortical and subcortical subregion in more detail. No significant correlations emerged for anxiety, depression, or withdrawal severity. By contrast, changes in craving showed a negative association with changes in cortical GM volume (r = -0.386, uncorrected p = .042, FDR \u0026gt; .05). An exploratory, uncorrected survey \u0026nbsp;across cortical regions \u0026nbsp;revealed negative correlations in several frontal regions (caudal middle frontal gyrus: r = -0.41, p = .028; paracentral lobule: r = -0.40, p = .033; precentral region: r = -0.39, p = .039), the temporal lobe (inferior temporal cortex: r = -0.38, p = .046; banks of the superior temporal sulcus: r = -0.42, p = .025), and the parietal lobe (inferior parietal cortex: r = -0.39, p = .041; supramarginal gyrus: r = -0.47, p = .011; precuneus: r = -0.52, p = .004). Additionally, the volume of the posterior corpus callosum correlated negatively with changes in craving (r = -0.494, p = .008). Overall, these findings indicate that individuals who showed larger volume increases in these specific regions also experienced greater reductions in craving.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eAssociations between brain volume changes and inflammatory changes\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine a potential link between inflammation and choroid plexus (ChP) volume, we tested cytokine-ChP associations at both time points and then explored whether changes in cytokines between T1 and T2 predicted changes in ChP volume. Positive correlations emerged at T1 between ChP volume and both MCP‐1 (ρ=0.364, p=0.040, FDR p=0.080) and MIP‐1β (ρ=0.378, p=0.033, FDR p=0.080), indicating a trend toward significance after FDR correction.\u003c/p\u003e\n\u003cp\u003eWe then evaluated whether shifts in cytokines from T1 to T2 related to significant regional volume changes. In this exploration, MCP‐1 stood out, displaying a significant positive correlation with cortical (ρ=0.367, p=0.040, FDR p=0.094), subcortical (ρ=0.416, p=0.019, FDR p=0.076), and cerebellar GM volumes (ρ=0.355, p=0.047, FDR p=0.094), and a significant negative correlation with ventricular volume (ρ=-0.460, p=0.009, FDR p=0.072). Within the cortex, the temporal lobe showed the strongest association (ρ=0.562, p\u0026lt;0.001, FDR p=0.003): declining MCP‐1 coincided with shrinking GM volumes and ventricle enlargement (see Figure 3).\u003c/p\u003e\n\u003cp\u003eWe next made an exploratory investigation of all cortical subregions by lobe that correlated positively with the individual changes in MCP1 concentration. In the temporal lobe, there were correlations in the fusiform gyrus (ρ = 0.367, p = 0.039), inferior temporal gyrus (ρ = 0.406, p = 0.022), middle temporal gyrus (ρ = 0.387, p = 0.028) and superior temporal gyrus (ρ = 0.427, p = 0.015). In the frontal lobe, correlations were confined to parts of the inferior frontal gyrus: the pars orbitalis (ρ = 0.383, p = 0.031), the pars opercularis (ρ = 0.399, p = 0.024), and the pars triangularis (ρ = 0.462, p = 0.008) and in the medial orbitofrontal cortex (ρ = 0.564, p = 0.001). In addition, the cingulate isthmus (ρ = 0.365, p = 0.040) also showed a significant correlation. Among subcortical regions, only the volume change in thalamus correlated with the decrease in plasma MCP -1 (ρ = 0.364, p = 0.040). Moreover, the insula also exhibited a significant positive correlation with the MCP-1 reduction (ρ = 0.361, p = 0.042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9.\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003eAssociations between inflammatory changes and\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cu\u003echanges in clinical symptoms\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found no link between cytokine levels and changes from T1 to T2 in depression, anxiety, or craving symptoms in the SAUD patients, nor were there any such associations in the control group. However, the SAUD patients showed a statistically significant, positive correlation between the Cushman withdrawal score and MCP-1 level at T1 (ρ=.469, uncorrected p=.009, FDR-corrected p=.036). See Figure 4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eClinical changes during withdrawal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults confirmed a positive trajectory in the psychological well-being of SAUD patients across the detoxification process, with significant reductions in the levels of anxiety, depression and craving in the first three weeks of withdrawal, aligning with previous findings (19,20,46).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine dynamics and link with withdrawal symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the onset of withdrawal, patients exhibited elevated plasma levels of all four cytokines, reflecting a low-grade systemic pro-inflammatory state (45,47,48). A key new observation is that baseline MCP-1 correlated with withdrawal severity, and not total alcohol intake, pointing to a specific link between inflammation and symptom intensity. MCP-1 drives monocyte recruitment, microglial activation, and increased BBB permeability ( Zhang \u0026amp; Luo, 2019) which may amplify systemic and central immune responses during abrupt cessation. In animal models, MCP-1 spikes by up to 1000% within 18 hours of ethanol withdrawal, coinciding with falling blood alcohol levels and supporting the concept of a withdrawal-specific neuroimmune rebound (7,51).\u003c/p\u003e\n\u003cp\u003eOver 19 days of withdrawal, MCP-1 and IL-8 levels both decreased toward control levels, consistent with other studies (45,52). This reduction may reflect partial normalization of gut permeability and LPS levels, which improve within weeks of abstinence (21). Although MIP-1\u0026beta; also dropped, it remained higher than in controls. TNF-\u0026alpha; showed no overall change, as it \u0026nbsp;rose in half of the patients while falling in the other half, in line with previous observations (48,53). A study with longer follow-ups, as reported by Yen et al. (45), suggested that more prolonged abstinence may be necessary to arrive at stable declines in some cytokines, such as TNF-\u0026alpha;.\u0026nbsp;Individual variability could also reflect genetic or lifestyle factors\u0026nbsp;(54). The fact that peripheral inflammation only partially resolves during inpatient treatment, with cytokines normalizing at varying rates, aligns with a recent review\u0026nbsp;(47).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLink between cytokines and the choroid plexus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMCP-1 and MIP-1\u0026beta; levels positively correlated with choroid-plexus (ChP) volume at T1. The ChP, principal producer of CSF, acts as an immune gateway, coordinating leukocyte entry and cytokine exchange between blood and CSF (55,56). \u0026nbsp;Its enlargement is a recognized marker of neuroinflammation across psychiatric disorders and likely reflects leukocyte infiltration, oedema, and cytokine-driven epithelial proliferation/remodeling\u0026nbsp;(27). MCP-1 and MIP-1\u0026beta;, as key chemokines for monocyte recruitment\u0026nbsp;(57), may exert a specific influence on ChP structure. Our data provide the first evidence of a ChP-cytokine link in SAUD and, with the subsequent ChP shrinkage observed at T2, highlight this structure as a dynamic biomarker of early neuro-immune resolution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural brain changes during withdrawal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo significant overall white matter evolution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApart from the cerebellum, we found no significant WM changes across withdrawal, suggesting that substantial axon remodelling or remyelination does not occur within two to three weeks of abstinence. This aligns with an earlier study showing limited WM recovery at two weeks (43), but may differ from microstructural findings that revealed subtler early changes (25,58,59). Such discrepancies may reflect the limitations of T1-based volumetry in detecting localized tract‐specific remodeling, which might require longer sobriety or more sensitive imaging modalities. Notably, the cerebellum GM volume appeared to recover more rapidly, implying distinct regional plasticity in early abstinence (43).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTwo coexisting patterns in brain changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results suggest two coexisting trends in the evolution of brain recovery during abstinence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecovery‐dominated pattern\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the one hand, the group-level analysis revealed a general recovery dynamics associated with alcohol cessation,\u0026nbsp;marked by an overall increase in GM \u0026nbsp;volume. The increase in cerebral cortex was driven by specific regions (middle and superior frontal areas, associative parietal areas, and most of the occipital lobe), and the subcortical increase was driven by the diencephalon and the thalamus, with an additional component from the cerebellum. Previous studies have already noted widespread GM \u0026nbsp;recovery within two weeks of abstinence (22,24,43,44,60,61), although the baseline intervals for those cohorts varied, which complicates a direct comparison with present results following a more tightly controlled timeline. The GM volume increases are very likely attributable to neuronal restoration processes, including rehydration (62), improved cerebral perfusion (63), dendritic regrowth (58,64), and neurotransmitter rebalancing (65). Concurrent reductions in ventricular volume, likely a direct effect of expansion of the surrounding GM (61,66), indeed correlated with the GM gains which mirrored previous findings at two (67) to four weeks (68) post‐withdrawal. The GM increase also correlated with the drop in craving in our SAUD patients, indicating a possible clinical importance of these changes. Volume increase in frontal subregions (caudal middle frontal, paracentral lobule, and precentral gyrus) is expected to reinforce executive inhibition and motor suppression of alcohol-seeking behavior (69). Temporal lobe structures (inferior temporal cortex, superior temporal sulcus) facilitate visual association and socio‐emotional processing (70), recovery of which might dampen cue‐reactivity (69). Gains in parietal areas (inferior parietal cortex, supramarginal gyrus, precuneus) mediate attentional bias, multisensory integration, and self‐referential cognition (71), which might also mediate aspects of recovery. Finally, expansion of the posterior corpus callosum likely enhances inter-hemispheric signaling, thereby improving top-down regulatory control over reward circuits (72).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile grey-matter increase paralleled the decline in craving, the temporal profile of MCP-1 did not (consistent with (47) meta-analysis) suggesting that clinical improvement aligns more with structural restoration than with chemokine normalization, a dissociation future multimodal studies should probe explicitly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInflammation‐resolution pattern\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the other hand, individual-level correlations between regional brain volume decreases and MCP-1 reduction in specific regions (inferior frontal areas, temporal lobe structures, the insula, and the cingulate isthmus) suggest a second mechanism, possibly reflecting the resolution of inflammation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the early stages of abstinence, elevated MCP-1 may contribute to microglial activation and BBB dysfunction. Although fully phagocytic amoeboid microglia are not typically observed in SAUD (73),\u0026nbsp;studies report a partially activated, pro-inflammatory microglial state whether driven by chronic alcohol exposure\u0026nbsp;(9,74)\u0026nbsp;or by a rebound effect during withdrawal\u0026nbsp;(75,76). This phenotype is sufficient to trigger cytokine release, astrocyte activation and increased BBB permeability \u0026nbsp;(77). MCP-1 itself has been shown to directly increase BBB permeability\u0026nbsp;(50), while astrocytic aquaporins facilitate vasogenic oedema\u0026nbsp;(78), transiently expanding regional volume. Diffusion-MRI and histology further show that microglial soma hypertrophy and process retraction enlarge the extracellular space and can raise regional volume even without cell-number changes\u0026nbsp;(76,79). As inflammation resolves, microglia revert to a ramified state\u0026nbsp;(80)\u0026nbsp;and barrier integrity is restored, possibly\u0026nbsp;producing the GM contraction associated with the decrease in MCP-1. In our sample, this process unfold within two weeks, consistent with the known rapid dynamic of microglial (partial) deactivation\u0026nbsp;(76,81,82)\u0026nbsp;and the normalization of LPS and MCP-1 early in abstinence\u0026nbsp;(21). Participants with the greatest GM deflation showed lesser shrinkage or paradoxical enlargement of ventricles, consistent with Monro-Kellie compensation. Although MCP-1 was not measured in brain tissue directly, its correlations with structural changes, choroid plexus volume, and withdrawal severity supports a model in which peripheral immune signaling, via BBB or ChP pathways, contributes to transient neuroinflammatory brain remodeling during early abstinence.\u0026nbsp;The affected regions are known to host microglia with high activation potential\u0026nbsp;(83)\u0026nbsp;and are particularly vulnerable to inflammation across psychiatric disorders\u0026nbsp;(84,85). The insula, that plays a central role in interoception and affective processing, responds to peripheral immune signals\u0026nbsp;(86).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese two patterns of response (post‐alcohol recovery and inflammation resolution) may cancel out at the group level, such that some changes did not emerge as significant in the T1-T2 comparison. However, we see in our population a clustering of responses (see Figure 5): \u0026nbsp;\u0026ldquo;recovery‐dominant\u0026rdquo; regions showing an overall volume increase independent of MCP‐1 decline; (2) \u0026ldquo;inflammation‐modulated\u0026rdquo; regions with no significant volume change, but strong correlations to MCP‐1 reduction; and (3) \u0026ldquo;mixed‐effect\u0026rdquo; areas (thalamus, cerebellum, pars opercularis) that may exhibit a volume gain \u0026nbsp;along with a \u0026ldquo;microglial deflation\u0026rdquo; component, thus tempering the net volumetric increase at T2.\u003c/p\u003e\n\u003cp\u003eBy demonstrating tight co-modulation of peripheral chemokines, ChP dynamics and regional grey-matter volumes, our study reinforces the need for integrated neuro-immune models of SAUD and highlight the ChP-MCP-1 axis as a promising biomarker and therapeutic lever for future interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender and SAUD severity effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGender-related volume brain volume changes, aligned with known sex differences in alcohol metabolism, toxicity, and immunity (87,88), lost significance after FDR correction, indicating limited power. Age and pre-detoxification alcohol intake were not associated with brain or cytokine measures, suggesting that abrupt cessation, rather than cumulative exposure, drives neuro-immune observations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations and future directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we did not monitor smoking changes, though ongoing smoking makes it unlikely to explain our time‐dependent effects. A key strength lies is our precise control of the timing of alcohol cessation relative to measurements: all participants drank until admission or the day before, but we lacked exact final drinking episode, drinking‐day averages, and age‐at‐first‐drink data, which could moderate outcomes. A minority (N=10) showed paradoxical anxiety increases, suggesting individual trajectories that merit targeted follow‐up. Finally, MCP-1 was the most robust biomarker, but our four-cytokine panel may have missed other relevant mediators.\u003c/p\u003e\n\u003cp\u003eWe applied an atlas-wide, FDR-corrected analysis to keep false positives low, but we also show uncorrected trends to show effects that need larger samples. Because strict correction in a single cohort can hide biologically relevant signals (89,90), replication in an independent sample is essential, an approach now widely recommended for exploratory neuroimaging (91,92).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePdT received financial support for this study from the Belgian National Fund for Scientific Research (FNRS-FRS; Grant Number: 3.4585.07; http://www1.frs-fnrs.be/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon written request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the participants who took part in this study for their commitment and contributions to the nursing staff at Saint-Luc Academic Hospital, Brussels, for their assistance with blood collection. We also extend our appreciation to the MRI technologists for their crucial support and expertise in imaging, and to Prof. Paul Cumming of Bern University Hospital for critical reading of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKoob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. The Lancet Psychiatry. 1 ao\u0026ucirc;t 2016;3(8):760‑73.\u003c/li\u003e\n \u003cli\u003eYang, Singla R, Maheshwari O, Fontaine CJ, Gil-Mohapel J. Alcohol use disorder: neurobiology and therapeutics. Biomedicines. 2022;10(5):1192.\u003c/li\u003e\n \u003cli\u003eCrews FT, Sarkar DK, Qin L, Zou J, Boyadjieva N, Vetreno RP. Neuroimmune function and the consequences of alcohol exposure. Alcohol research: current reviews. 2015;37(2):331.\u003c/li\u003e\n \u003cli\u003eErickson EK, Grantham EK, Warden AS, Harris RA. Neuroimmune signaling in alcohol use disorder. Pharmacology Biochemistry and Behavior. 2019;177:34‑60.\u003c/li\u003e\n \u003cli\u003eAhlers KE, Kara\u0026ccedil;ay B, Fuller L, Bonthius DJ, Dailey ME. Transient activation of microglia following acute alcohol exposure in developing mouse neocortex is primarily driven by BAX-dependent neurodegeneration: Microglia and Developmental Alcohol Exposure. Glia. oct 2015;63(10):1694‑713.\u003c/li\u003e\n \u003cli\u003eHolloway KN, Douglas JC, Rafferty TM, Kane CJM, Drew PD. Ethanol Induces Neuroinflammation in a Chronic Plus Binge Mouse Model of Alcohol Use Disorder via TLR4 and MyD88-Dependent Signaling. Cells. janv 2023;12(16):2109.\u003c/li\u003e\n \u003cli\u003eMarshall SA, Geil CR, Nixon K. Prior binge ethanol exposure potentiates the microglial response in a model of alcohol-induced neurodegeneration. Brain sciences. 2016;6(2):16.\u003c/li\u003e\n \u003cli\u003eQin L, Crews FT. NADPH oxidase and reactive oxygen species contribute to alcohol-induced microglial activation and neurodegeneration. Journal of Neuroinflammation. 2012;9:5.\u003c/li\u003e\n \u003cli\u003eCrews FT, Qin L, Coleman L, Vidrascu E, Vetreno R. Cortical reactive microglia activate astrocytes, increasing neurodegeneration in human alcohol use disorder [Internet]. bioRxiv; 2025 [cit\u0026eacute; 5 juin 2025]. p. 2025.04.25.650687. Disponible sur: https://www.biorxiv.org/content/10.1101/2025.04.25.650687v1\u003c/li\u003e\n \u003cli\u003eDe Carvalho LM, Wiers CE, Sun H, Wang GJ, Volkow ND. Increased transcription of TSPO, HDAC2, and HDAC6 in the amygdala of males with alcohol use disorder. Brain and Behavior. 2021;11(2):e01961.\u003c/li\u003e\n \u003cli\u003eVetreno RP, Qin L, Coleman Jr LG, Crews FT. Increased Toll-like Receptor-MyD88-NF\u0026kappa;B-Proinflammatory neuroimmune signaling in the orbitofrontal cortex of humans with alcohol use disorder. Alcoholism: Clinical and Experimental Research. 2021;45(9):1747‑61.\u003c/li\u003e\n \u003cli\u003eHillmer AT, Sandiego CM, Hannestad J, Angarita GA, Kumar A, McGovern EM, et al. \u003cem\u003eIn vivo\u003c/em\u003e imaging of translocator protein, a marker of activated microglia, in alcohol dependence. Molecular Psychiatry. d\u0026eacute;c 2017;22(12):1759‑66.\u003c/li\u003e\n \u003cli\u003eKalk NJ, Guo Q, Owen D, Cherian R, Erritzoe D, Gilmour A, et al. Decreased hippocampal translocator protein (18 kDa) expression in alcohol dependence: a [\u003csup\u003e11\u003c/sup\u003eC]PBR28 PET study. Translational Psychiatry. janv 2017;7(1):e996.\u003c/li\u003e\n \u003cli\u003eKim SW, Wiers CE, Tyler R, Shokri-Kojori E, Jang YJ, Zehra A, et al. Influence of alcoholism and cholesterol on TSPO binding in brain: PET [ 11 C]PBR28 studies in humans and rodents. Neuropsychopharmacology. ao\u0026ucirc;t 2018;43(9):1832.\u003c/li\u003e\n \u003cli\u003eCoppens V, Morrens M, Destoop M, Dom G. The interplay of inflammatory processes and cognition in alcohol use disorders\u0026mdash;a systematic review. Frontiers in psychiatry. 2019;10:632.\u003c/li\u003e\n \u003cli\u003eRaval NR, Angarita G, Matuskey D, Miller R, Drake LR, Kapinos M, et al. Imaging the brain\u0026rsquo;s immune response to alcohol with [11C]PBR28 TSPO Positron Emission Tomography. Mol Psychiatry. 2 ao\u0026ucirc;t 2023;1‑7.\u003c/li\u003e\n \u003cli\u003eSaba W, Goutal S, Auvity S, Kuhnast B, Coulon C, Kouyoumdjian V, et al. Imaging the neuroimmune response to alcohol exposure in adolescent baboons: a TSPO PET study using 18F-DPA-714. Addiction Biology. 2018;23(5):1000‑9.\u003c/li\u003e\n \u003cli\u003eLi X, Ramos-Rol\u0026oacute;n AP, Kass G, Pereira-Rufino LS, Shifman N, Shi Z, et al. Imaging neuroinflammation in individuals with substance use disorders. The Journal of clinical investigation [Internet]. 2024 [cit\u0026eacute; 5 juin 2025];134(11). Disponible sur: https://www.jci.org/articles/view/172884\u003c/li\u003e\n \u003cli\u003ede Timary P, Cordovil de Sousa Uva M, Deno\u0026euml;l C, Hebborn L, Derely M, Desseilles M, et al. The Associations between Self-Consciousness, Depressive State and Craving to Drink among Alcohol Dependent Patients Undergoing Protracted Withdrawal. PLoS ONE [Internet]. 2013;8(8). Disponible sur: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883138442\u0026amp;doi=10.1371%2fjournal.pone.0071560\u0026amp;partnerID=40\u0026amp;md5=1e90b7195ec586287a7ffd0fb412c213\u003c/li\u003e\n \u003cli\u003ePetit G, Luminet O, Cordovil de Sousa Uva M, Monhonval P, Leclercq S, Spilliaert Q, et al. Gender Differences in Affects and Craving in Alcohol-Dependence: A Study During Alcohol Detoxification. Alcohol Clin Exp Res. 1 janv 2017;n/a-n/a.\u003c/li\u003e\n \u003cli\u003eLeclercq S, Cani PD, Neyrinck AM, St\u0026auml;rkel P, Jamar F, Mikolajczak M, et al. Role of intestinal permeability and inflammation in the biological and behavioral control of alcohol-dependent subjects. Brain, Behavior, and Immunity. 1 ao\u0026ucirc;t 2012;26(6):911‑8.\u003c/li\u003e\n \u003cli\u003eB\u0026uuml;hler M, Mann K. Alcohol and the Human Brain: A Systematic Review of Different Neuroimaging Methods. Alcoholism: Clinical and Experimental Research. 2011;35(10):1771‑93.\u003c/li\u003e\n \u003cli\u003eMonnig MA, Tonigan JS, Yeo RA, Thoma RJ, McCrady BS. White matter volume in alcohol use disorders: a meta‐analysis. Addiction Biology. mai 2013;18(3):581‑92.\u003c/li\u003e\n \u003cli\u003eParvaz MA, Rabin RA, Adams F, Goldstein RZ. Structural and functional brain recovery in individuals with substance use disorders during abstinence: A review of longitudinal neuroimaging studies. Drug and Alcohol Dependence. 2022;232:109319.\u003c/li\u003e\n \u003cli\u003eDe Santis S, Bach P, P\u0026eacute;rez-Cervera L, Cosa-Linan A, Weil G, Vollst\u0026auml;dt-Klein S, et al. Microstructural white matter alterations in men with alcohol use disorder and rats with excessive alcohol consumption during early abstinence. JAMA psychiatry. 2019;76(7):749‑58.\u003c/li\u003e\n \u003cli\u003eSelim MK, Harel M, De Santis S, Perini I, Sommer WH, Heilig M, et al. Repetitive deep TMS in alcohol dependent patients halts progression of white matter changes in early abstinence. Psychiatry and Clinical Neurosciences. 2024;78(3):176‑85.\u003c/li\u003e\n \u003cli\u003eAlthubaity N, Schubert J, Martins D, Yousaf T, Nettis MA, Mondelli V, et al. Choroid plexus enlargement is associated with neuroinflammation and reduction of blood brain barrier permeability in depression. NeuroImage: Clinical. 2022;33:102926.\u003c/li\u003e\n \u003cli\u003eDiez-Cirarda M, Yus-Fuertes M, Delgado-Alonso C, Gil-Mart\u0026iacute;nez L, Jim\u0026eacute;nez-Garc\u0026iacute;a C, Gil-Moreno MJ, et al. Choroid plexus volume is enlarged in long COVID and associated with cognitive and brain changes. Molecular Psychiatry. 2025;1‑10.\u003c/li\u003e\n \u003cli\u003eRicigliano VA, Stankoff B. Choroid plexuses at the interface of peripheral immunity and tissue repair in multiple sclerosis. Current Opinion in Neurology. 2023;36(3):214‑21.\u003c/li\u003e\n \u003cli\u003eZhou YF, Huang JC, Zhang P, Fan FM, Chen S, Fan HZ, et al. Choroid plexus enlargement and allostatic load in schizophrenia. Schizophrenia bulletin. 2020;46(3):722‑31.\u003c/li\u003e\n \u003cli\u003eFolstein MF, Folstein SE, McHugh PR. \u0026laquo; Mini-mental state \u0026raquo;. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. nov 1975;12(3):189‑98.\u003c/li\u003e\n \u003cli\u003eBeck AT, Steer RA, Brown GK. Beck depression inventory-II. San Antonio, TX. 1996;78204‑2498.\u003c/li\u003e\n \u003cli\u003eSpielberger CD. State-trait anxiety inventory for adults. 1983;\u003c/li\u003e\n \u003cli\u003eAnton RF, Moak DH, Latham PK. The obsessive compulsive drinking scale: a new method of assessing outcome in alcoholism treatment studies. Archives of general psychiatry. 1996;53(3):225‑31.\u003c/li\u003e\n \u003cli\u003eBourque P, Beaudette D. \u0026Eacute;tude psychometrique du questionnaire de d\u0026eacute;pression de Beck aupr\u0026egrave;s d\u0026rsquo;un \u0026eacute;chantillon d\u0026rsquo;\u0026eacute;tudiants universitaires francophones. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement. 1982;14(3):211.\u003c/li\u003e\n \u003cli\u003eBruchon-Schweitzer M, Paulhan I. Le manuel du STAI-Y de CD Spielberger, adaptation fran\u0026ccedil;aise. Paris: ECPA. 1993;\u003c/li\u003e\n \u003cli\u003eAnsseau M, Besson J, Lejoyeux M, Pinto E, Landry U, Cornes M, et al. A French translation of the Obsessive-Compulsive Drinking Scale for craving in alcohol-dependent patients: A validation study in Belgium, France, and Switzerland. European addiction research. 2000;6(2):51‑6.\u003c/li\u003e\n \u003cli\u003eCushman Jr P, Forbes R, Lemer W, Stewart M. Alcohol Withdrawal Syndromes: Clinical Management with Lofexidine. Alcoholism: Clinical and Experimental Research. 1985;9(2):103‑8.\u003c/li\u003e\n \u003cli\u003eSobell LC, Sobell MB. Timeline Follow-Back. In: Litten RZ, Allen JP, \u0026eacute;diteurs. Measuring Alcohol Consumption [Internet]. Totowa, NJ: Humana Press; 1992 [cit\u0026eacute; 2 oct 2023]. p. 41‑72. Disponible sur: http://link.springer.com/10.1007/978-1-4612-0357-5_3\u003c/li\u003e\n \u003cli\u003eDale AM, Fischl B, Sereno MI. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage. 1999;9(2):179‑94.\u003c/li\u003e\n \u003cli\u003eFischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain. Neuron. 31 janv 2002;33(3):341‑55.\u003c/li\u003e\n \u003cli\u003eFischl B, van der Kouwe A, Destrieux C, Halgren E, S\u0026eacute;gonne F, Salat DH, et al. Automatically Parcellating the Human Cerebral Cortex. Cereb Cortex. 2004;14(1):11‑22.\u003c/li\u003e\n \u003cli\u003evan Eijk J, Demirakca T, Frischknecht U, Hermann D, Mann K, Ende G. Rapid Partial Regeneration of Brain Volume During the First 14 Days of Abstinence from Alcohol. Alcohol Clin Exp Res. 1 janv 2013;37(1):67‑74.\u003c/li\u003e\n \u003cli\u003eK\u0026uuml;hn S, Charlet K, Schubert F, Kiefer F, Zimmermann P, Heinz A, et al. Plasticity of Hippocampal Subfield Volume Cornu Ammonis 2+3 Over the Course of Withdrawal in Patients With Alcohol Dependence. JAMA Psychiatry. 1 juill 2014;71(7):806‑11.\u003c/li\u003e\n \u003cli\u003eYen CH, Ho PS, Yeh YW, Liang CS, Kuo SC, Huang CC, et al. Differential cytokine levels between early withdrawal and remission states in patients with alcohol dependence. Psychoneuroendocrinology. 1 f\u0026eacute;vr 2017;76:183‑91.\u003c/li\u003e\n \u003cli\u003ede Timary P, Luts A, Hers D, Luminet O. Absolute and relative stability of alexithymia in alcoholic inpatients undergoing alcohol withdrawal: Relationship to depression and anxiety. Psychiatry Research. 15 janv 2008;157(1):105‑13.\u003c/li\u003e\n \u003cli\u003eAdams C, Conigrave JH, Lewohl J, Haber P, Morley KC. Alcohol use disorder and circulating cytokines: A systematic review and meta-analysis. Brain, Behavior, and Immunity. 1 oct 2020;89:501‑12.\u003c/li\u003e\n \u003cli\u003eLeclercq S, Cani PD, Neyrinck AM, St\u0026auml;rkel P, Jamar F, Mikolajczak M, et al. Role of intestinal permeability and inflammation in the biological and behavioral control of alcohol-dependent subjects. Brain, behavior, and immunity. 2012;26(6):911‑8.\u003c/li\u003e\n \u003cli\u003eDeshmane SL, Kremlev S, Amini S, Sawaya BE. Monocyte Chemoattractant Protein-1 (MCP-1): An Overview. Journal of Interferon \u0026amp; Cytokine Research. juin 2009;29(6):313‑26.\u003c/li\u003e\n \u003cli\u003eZhang K, Luo J. Role of MCP-1 and CCR2 in alcohol neurotoxicity. Pharmacological research. 2019;139:360‑6.\u003c/li\u003e\n \u003cli\u003eWalter TJ, Crews FT. Microglial depletion alters the brain neuroimmune response to acute binge ethanol withdrawal. J Neuroinflammation. d\u0026eacute;c 2017;14(1):86.\u003c/li\u003e\n \u003cli\u003eGirard M, Malauzat D, Nubukpo P. Serum inflammatory molecules and markers of neuronal damage in alcohol-dependent subjects after withdrawal. The World Journal of Biological Psychiatry. 2 janv 2019;20(1):76‑90.\u003c/li\u003e\n \u003cli\u003eLi W, Amet T, Xing Y, Yang D, Liangpunsakul S, Puri P, et al. Alcohol abstinence ameliorates the dysregulated immune profiles in patients with alcoholic hepatitis: A prospective observational study. Hepatology. 2017;66(2):575‑90.\u003c/li\u003e\n \u003cli\u003eD\u0026rsquo;Esposito V, Di Tolla MF, Lecce M, Cavalli F, Libutti M, Misso S, et al. Lifestyle and Dietary Habits Affect Plasma Levels of Specific Cytokines in Healthy Subjects. Front Nutr [Internet]. 24 juin 2022 [cit\u0026eacute; 5 d\u0026eacute;c 2024];9. Disponible sur: https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2022.913176/full\u003c/li\u003e\n \u003cli\u003eDamkier HH, Brown PD, Praetorius J. Cerebrospinal Fluid Secretion by the Choroid Plexus. Physiological Reviews. oct 2013;93(4):1847‑92.\u003c/li\u003e\n \u003cli\u003eSchwartz M, Baruch K. The resolution of neuroinflammation in neurodegeneration: leukocyte recruitment via the choroid plexus. The EMBO Journal. 2 janv 2014;33(1):7‑22.\u003c/li\u003e\n \u003cli\u003eKabashima H, Yoneda M, Nagata K, Hirofuji T, Maeda K. THE PRESENCE OF CHEMOKINE (MCP-1, MIP-1\u0026alpha;, MIP-1\u0026beta;, IP-10, RANTES)-POSITIVE CELLS AND CHEMOKINE RECEPTOR (CCR5, CXCR3)-POSITIVE CELLS IN INFLAMED HUMAN GINGIVAL TISSUES. Cytokine. 1 oct 2002;20(2):70‑7.\u003c/li\u003e\n \u003cli\u003eSantis SD, Cosa-Linan A, Garcia-Hernandez R, Dmytrenko L, Vargova L, Vorisek I, et al. Chronic alcohol consumption alters extracellular space geometry and transmitter diffusion in the brain. Science Advances. 1 juin 2020;6(26):eaba0154.\u003c/li\u003e\n \u003cli\u003eSelim MK, Harel M, De Santis S, Perini I, Sommer WH, Heilig M, et al. Repetitive deep TMS in alcohol dependent patients halts progression of white matter changes in early abstinence. Psychiatry and Clinical Neurosciences. 2024;78(3):176‑85.\u003c/li\u003e\n \u003cli\u003eBach P, Koopmann A, Bumb JM, Vollst\u0026auml;dt-Klein S, Reinhard I, Rietschel M, et al. Leptin predicts cortical and subcortical gray matter volume recovery in alcohol dependent patients: A longitudinal structural magnetic resonance imaging study. Hormones and behavior. 2020;124:104749.\u003c/li\u003e\n \u003cli\u003ePfefferbaum A, Sullivan EV, Mathalon DH, Shear PK, Rosenbloom MJ, Lim KO. Longitudinal Changes in Magnetic Resonance Imaging Brain Volumes in Abstinent and Relapsed Alcoholics. Alcoholism: Clinical and Experimental Research. 1995;19(5):1177‑91.\u003c/li\u003e\n \u003cli\u003eSchweinsburg BC, Taylor MJ, Videen JS, Alhassoon OM, Patterson TL, Grant I. Elevated \u003cem\u003emyo\u003c/em\u003e ‐Inositol in Gray Matter of Recently Detoxified but Not Long‐Term Abstinent Alcoholics: A Preliminary MR Spectroscopy Study. Alcoholism Clin \u0026amp;amp; Exp Res. mai 2000;24(5):699‑705.\u003c/li\u003e\n \u003cli\u003eEnde G, Welzel H, Walter S, Weber-Fahr W, Diehl A, Hermann D, et al. Monitoring the effects of chronic alcohol consumption and abstinence on brain metabolism: a longitudinal proton magnetic resonance spectroscopy study. Biological psychiatry. 2005;58(12):974‑80.\u003c/li\u003e\n \u003cli\u003eBartsch AJ, Homola G, Biller A, Smith SM, Weijers HG, Wiesbeck GA, et al. Manifestations of early brain recovery associated with abstinence from alcoholism. Brain. 2007;130(1):36‑47.\u003c/li\u003e\n \u003cli\u003eK\u0026ouml;hler S, Klimke S, Hellweg R, Lang UE. Serum brain-derived neurotrophic factor and nerve growth factor concentrations change after alcohol withdrawal: preliminary data of a case-control comparison. European Addiction Research. 2013;19(2):98‑104.\u003c/li\u003e\n \u003cli\u003eZahr NM, Mayer D, Rohlfing T, Hasak MP, Hsu O, Vinco S, et al. Brain Injury and Recovery Following Binge Ethanol: Evidence from In Vivo Magnetic Resonance Spectroscopy. Biological Psychiatry. 1 mai 2010;67(9):846‑54.\u003c/li\u003e\n \u003cli\u003evan Eijk J, Demirakca T, Frischknecht U, Hermann D, Mann K, Ende G. Rapid Partial Regeneration of Brain Volume During the First 14 Days of Abstinence from Alcohol. Alcohol: Clinical and Experimental Research. 2013;37(1):67‑74.\u003c/li\u003e\n \u003cli\u003eGazdzinski S, Durazzo TC, Meyerhoff DJ. Temporal dynamics and determinants of whole brain tissue volume changes during recovery from alcohol dependence. Drug and Alcohol Dependence. 1 juin 2005;78(3):263‑73.\u003c/li\u003e\n \u003cli\u003eKoob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. ao\u0026ucirc;t 2016;3(8):760‑73.\u003c/li\u003e\n \u003cli\u003eDeen B, Koldewyn K, Kanwisher N, Saxe R. Functional organization of social perception and cognition in the superior temporal sulcus. Cerebral cortex. 2015;25(11):4596‑609.\u003c/li\u003e\n \u003cli\u003eLusher J, Chandler C, Ball D. Alcohol dependence and the alcohol Stroop paradigm: evidence and issues. Drug and Alcohol Dependence. 6 sept 2004;75(3):225‑31.\u003c/li\u003e\n \u003cli\u003eGaudreault PO, King SG, Malaker P, Alia-Klein N, Goldstein RZ. Whole-brain white matter abnormalities in human cocaine and heroin use disorders: association with craving, recency, and cumulative use. Mol Psychiatry. f\u0026eacute;vr 2023;28(2):780‑91.\u003c/li\u003e\n \u003cli\u003eMarshall SA, McClain JA, Kelso ML, Hopkins DM, Pauly JR, Nixon K. Microglial activation is not equivalent to neuroinflammation in alcohol-induced neurodegeneration: The importance of microglia phenotype. Neurobiology of disease. 2013;54:239‑51.\u003c/li\u003e\n \u003cli\u003eCruz C, Meireles M, Silva SM. Chronic ethanol intake induces partial microglial activation that is not reversed by long-term ethanol withdrawal in the rat hippocampal formation. NeuroToxicology. 1 mai 2017;60:107‑15.\u003c/li\u003e\n \u003cli\u003eCrews FT, Macht V, Vetreno RP. Epigenetic regulation of microglia and neurons by proinflammatory signaling following adolescent intermittent ethanol (AIE) exposure and in human AUD. Advances in Drug and Alcohol Research. 2024;4:12094.\u003c/li\u003e\n \u003cli\u003eDe Santis S, Selim MK, Canals S. Brain Microstructure in Alcohol Addiction: Characterization of Diffusion-Based MRI Biomarkers, Neuropathological Substrates, and Functional Consequences. In: Mueller S, Heilig M, \u0026eacute;diteurs. Alcohol and Alcohol-related Diseases [Internet]. Cham: Springer International Publishing; 2023 [cit\u0026eacute; 4 janv 2024]. p. 493‑508. Disponible sur: https://doi.org/10.1007/978-3-031-32483-3_27\u003c/li\u003e\n \u003cli\u003eHuang X, Hussain B, Chang J. Peripheral inflammation and blood\u0026ndash;brain barrier disruption: effects and mechanisms. CNS Neuroscience \u0026amp; Therapeutics. 2021;27(1):36‑47.\u003c/li\u003e\n \u003cli\u003eStokum JA, Kurland DB, Gerzanich V, Simard JM. Mechanisms of Astrocyte-Mediated Cerebral Edema. Neurochem Res. f\u0026eacute;vr 2015;40(2):317‑28.\u003c/li\u003e\n \u003cli\u003eGursky ZH, Johansson JR, Klintsova AY. Postnatal alcohol exposure and adolescent exercise have opposite effects on cerebellar microglia in rat. Intl J of Devlp Neuroscience. oct 2020;80(6):558‑71.\u003c/li\u003e\n \u003cli\u003eGotkiewicz M, Capra J, Miettinen PO, Natunen T, Tanila H. Three-dimensional view of microglia\u0026mdash;amyloid plaque interactions. Glia. 2025;73(1):196‑209.\u003c/li\u003e\n \u003cli\u003eSanchez-Alavez M, Nguyen W, Mori S, Wills DN, Otero D, Ehlers CL, et al. Time course of microglia activation and brain and blood cytokine/chemokine levels following chronic ethanol exposure and protracted withdrawal in rats. Alcohol. 2019;76:37‑45.\u003c/li\u003e\n \u003cli\u003eZhao YN, Wang F, Fan YX, Ping GF, Yang JY, Wu CF. Activated microglia are implicated in cognitive deficits, neuronal death, and successful recovery following intermittent ethanol exposure. Behavioural Brain Research. 1 janv 2013;236:270‑82.\u003c/li\u003e\n \u003cli\u003eTan YL, Yuan Y, Tian L. Microglial regional heterogeneity and its role in the brain. Mol Psychiatry. f\u0026eacute;vr 2020;25(2):351‑67.\u003c/li\u003e\n \u003cli\u003eBond DJ, Andreazza AC, Torres IJ, Honer WG, Lam RW, Yatham LN. Association of total peripheral inflammation with lower frontal and temporal lobe volumes in early-stage bipolar disorder: A proof-of-concept study. Journal of Affective Disorders. 15 d\u0026eacute;c 2022;319:229‑34.\u003c/li\u003e\n \u003cli\u003eRadewicz K, Garey LJ, Gentleman SM, Reynolds R. Increase in HLA-DR Immunoreactive Microglia in Frontal and Temporal Cortex of Chronic Schizophrenics. Journal of Neuropathology \u0026amp; Experimental Neurology. 1 f\u0026eacute;vr 2000;59(2):137‑50.\u003c/li\u003e\n \u003cli\u003eKerezoudis P, Howe CL, Wu LJ, Lundstrom BN, Van Gompel JJ. Insula and the Immune System: More than mere Co-existence? Neurosci Bull. 1 oct 2022;38(10):1271‑3.\u003c/li\u003e\n \u003cli\u003eMaynard ME, Barton EA, Robinson CR, Wooden JI, Leasure JL. Sex differences in hippocampal damage, cognitive impairment, and trophic factor expression in an animal model of an alcohol use disorder. Brain Struct Funct. janv 2018;223(1):195‑210.\u003c/li\u003e\n \u003cli\u003ePascual M, Montesinos J, Marcos M, Torres J, Costa‐Alba P, Garc\u0026iacute;a‐Garc\u0026iacute;a F, et al. Gender differences in the inflammatory cytokine and chemokine profiles induced by binge ethanol drinking in adolescence. Addiction Biology. nov 2017;22(6):1829‑41.\u003c/li\u003e\n \u003cli\u003eButton KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. mai 2013;14(5):365‑76.\u003c/li\u003e\n \u003cli\u003eIoannidis JPA. Why Most Published Research Findings Are False. PLOS Medicine. 30 ao\u0026ucirc;t 2005;2(8):e124.\u003c/li\u003e\n \u003cli\u003eCarp J. On the plurality of (methodological) worlds: estimating the analytic flexibility of FMRI experiments. Frontiers in neuroscience. 2012;6:149.\u003c/li\u003e\n \u003cli\u003ePoldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, Munaf\u0026ograve; MR, et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci. f\u0026eacute;vr 2017;18(2):115‑26.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6915490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6915490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this longitudinal study, 37 patients with severe alcohol use disorder (SAUD) were tested at the beginning (T1) and end (T2) of withdrawal to explore the evolution of and relationship between systemic inflammation, volumetric changes for brain grey matter (GM) and choroid plexus (ChP), and clinical symptoms. At T1, patients exhibited high levels of anxiety, depression, and craving, and had elevated plasmatic pro-inflammatory cytokines, indicating low-grade systemic inflammation. MCP-1 levels correlated positively with ChP volume and severity of withdrawal symptoms, while MIP-1β also correlated with ChP volume, together suggesting an acute immune response at T1, and underscoring the ChP as a potential neuroimmune biomarker. During three weeks’ abstinence, IL-8, MIP-1β, and MCP-1 levels decreased, although MIP-1β did not return to control levels, and TNF-α showed no significant reduction. Volumetric MRI analyses suggested two concurrent trajectories during withdrawal. First, an overall “recovery‐driven” pattern of rapidly increasing grey matter (GM) volume in widespread forebrain regions with parallel declines in ventricle size and craving. Second, GM in limbic cortical areas, temporal and inferior frontal cortex showed no significant volumetric gain. However, these regional volumes correlated significantly with declining MCP‐1, indicative of a “deflation”, potentially related to declining microglial activation. These findings highlight the role of inflammatory processes in shaping early neuroplastic changes during withdrawal and point to a central role of MCP‐1 in inflammation-driven morphometric changes.\u003c/p\u003e","manuscriptTitle":"Early abstinence in severe alcohol use disorder: MCP-1 decline, choroid plexus shrinkage, and region-specific grey-matter volume changes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 08:24:04","doi":"10.21203/rs.3.rs-6915490/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-07-29T08:23:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-27T20:35:00+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-24T06:46:18+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-11T21:38:42+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-11T14:21:09+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-08T18:08:58+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-02T21:54:03+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-07-01T17:53:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-19T11:21:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T11:20:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2025-06-18T11:28:42+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-06-18T10:42:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4a4fac8-6a3d-4370-a4a4-4a1796e6f983","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50294531,"name":"Health sciences/Biomarkers/Prognostic markers"},{"id":50294532,"name":"Biological sciences/Neuroscience/Molecular neuroscience"}],"tags":[],"updatedAt":"2026-03-20T07:40:43+00:00","versionOfRecord":{"articleIdentity":"rs-6915490","link":"https://doi.org/10.1038/s41398-026-03907-9","journal":{"identity":"translational-psychiatry","isVorOnly":false,"title":"Translational Psychiatry"},"publishedOn":"2026-02-28 05:00:00","publishedOnDateReadable":"February 28th, 2026"},"versionCreatedAt":"2025-06-23 08:24:04","video":"","vorDoi":"10.1038/s41398-026-03907-9","vorDoiUrl":"https://doi.org/10.1038/s41398-026-03907-9","workflowStages":[]},"version":"v1","identity":"rs-6915490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6915490","identity":"rs-6915490","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}