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The history of the Earth is a story of the co-evolution of minerals and microbes: not only have numerous rocks arisen from life but also life itself may have formed from rocks. To understand the strong association between microbes and inorganic substrates, we investigated the moonmilk, a calcium carbonate deposit of possible microbial origin, occurring in the Iron Age Etruscan necropolis of Tarquinia, in Italy. These tombs provide a unique environment where the hypogeal walls of the tombs are covered by this speleothem. To study moonmilk formation, we investigated the bacterial community in the rock in which the tombs were carved: calcarenite and hybrid sandstone. We present the first evidence that moonmilk precipitation is driven by microbes within the rocks and not only on the rock surfaces. We also describe how the moonmilk produced within the rocks contributes to rock formation and evolution. The microbial communities of the calcarenite and hybrid sandstone displayed, at the phylum level, the same microbial pattern of the moonmilk sampled from the walls of the hypogeal tombs, suggesting that the moonmilk originates from the metabolism of an endolytic bacterial community. The calcite moonmilk is the only known carbonate speleothem on Earth with undoubted biogenic origin, thus representing a robust and credible biosignature of life. Its presence in the inner parts of rocks adds to its characteristics as a biosignature.

Filter-feeding gelatinous macrozooplankton (FFGM), namely salps, pyrosomes and doliolids, are increasingly recognized as an essential component of the marine ecosystem. Unlike crustacean zooplankton (e.g., copepods) that feed on prey that are an order of magnitude smaller, filter feeding allows FFGM to have access to a wider range of organisms, with predator-over-prey size ratios as high as 105:1. In addition, most FFGM produce carcasses and/or fecal pellets that sink 10 times faster than those of copepods. This implies a rapid and efficient export of organic matter to depth. Even if these organisms represent < 5 % of the overall planktonic biomass, their associated organic matter flux could be substantial. Here we present a first estimate of the influence of FFGM on the export of particulate organic matter to the deep ocean based on the marine biogeochemical model NEMO-PISCES (Nucleus for European Modelling of the Ocean, Pelagic Interaction Scheme for Carbon and Ecosystem Studies). In this new version of PISCES, two processes characterize FFGM: the preference for small organisms due to filter feeding and the rapid sinking of carcasses and fecal pellets. To evaluate our simulated FFGM distribution, we compiled FFGM abundance observations into a monthly biomass climatology using a taxon-specific biomass–abundance conversion. Model–observation comparison supports the model's ability to quantify the global and large-scale patterns of FFGM biomass distribution but reveals an urgent need to better understand the factors triggering the boom-and-bust FFGM dynamics before we can reproduce the observed spatio-temporal variability of FFGM. FFGM substantially contribute to carbon export at depth (0.4 Pg C yr−1 at 1000 m), particularly in low-productivity regions (up to 40 % of organic carbon export at 1000 m), where they dominate macrozooplankton biomass by a factor of 2. The FFGM-induced export increases in importance with depth, with a simulated transfer efficiency close to 1.

Introduction About 60% of bipolar disorder (BD) cases are initially misdiagnosed as major depressive disorder (MDD), preventing BD patients from receiving appropriate treatment. An urgency exists to identify reliable biomarkers for improving differential diagnosis (DD). Machine learning methods may help translate current knowledge on biomarkers of mood disorders into clinical practice by providing individual-level classification. No study so far has combined biological data with clinical data to provide a multifactorial predictive model for DD. Objectives Define a predictive algorithm for BD and MDD by integrating structural neuroimaging and inflammatory data with neuropsychological measures (NM). Two different algorithms were compared: multiple kernel learning (MKL) and elastic net regularized logistic regression (EN). Methods In a sample of 141 subjects (70 MDD; 71 BD), two different models were implemented for each algorithm: 1) structural neuroimaging measures only (i.e. voxel-based morphometry (VBM), white matter fractional anisotropy (FA), and mean diffusivity (MD)); 2) VBM, FA, and MD combined with NM. In a subsample of 71 subjects (36 BD; 38 MDD), two similar models were implemented: 1) VBM, FA, and, MD combined with only NM; 2) VBM, FA, and MD combined with NM and peripheral inflammatory markers. Finally, the best model was selected for comparison with healthy controls (HC). Results Overall, the EN model based on all the modalities achieved the highest accuracy (AUC = 90.2%), outperforming MKL (AUC=85%). EN correctly classified BD and MDD with a diagnostic accuracy of 78.3%, sensitivity of 75%, and specificity of 81.6%. The most significant predictors of BD (variable inclusion probability (VIP) > 80%) were the parahippocampal cingulate, interleukin 9, chemokine CCL5, posterior thalamic radiation, and internal capsule, whereas MDD was best predicted by chemokine CCL23, the anterior cerebellum, and the sagittal stratum. In contrast, NM did not help to differentiate between MDD and BD. However, they help to distinguish patients from HC. Psychomotor coordination and speed of information processing discriminated between MDD and HC (VIP>90%), whereas fluency, working memory, and executive functions differentiated between BD and HC (VIP>80%). Conclusions In summary, BD was predicted by a strong proinflammatory profile, whereas MDD was identified by structural neuroimaging data. A multimodal approach offers additional instruments to improve personalized diagnosis in clinical practice and enhance the ability to make DD. Disclosure of Interest None Declared

Introduction Depression is the predominant mood alteration in bipolar disorder (BD), leading to overlapping symptomatology with major depressive disorder (MDD). Consequently, in clinical assessment, almost 60% of BD patients are misdiagnosed as affected by MDD. This calls for the creation of a framework for the differentiation of BD and MDD patients based on reliable biomarkers. Since machine learning (ML) enables to make predictions at the single-subject level, it appears to be particularly suitable for this task. Objectives We implemented a ML pipeline for the differentiation between depressed BD and MDD patients based on structural neuroimaging features. Methods Diffusion tensor imaging (DTI) and T1-weighted magnetic resonance imaging (MRI) data were acquired for 282 depressed BD (n=180) and MDD (n=102) patients. Axial (AD), radial (RD), mean (MD) diffusivity, and fractional anisotropy (FA) maps were extracted from DTI images, and voxel-based morphometry (VBM) measures were obtained from T1-weighted images. Each feature was entered separately into a 5-fold nested cross-validated ML pipeline differentiating between BD and MDD patients, comprising: confound regression for nuisance variables removal (i.e., age and sex), feature standardization, principal component analysis, and an elastic-net penalized regression. The models underwent 5000 random permutations as a test for significance, and the McNemar’s test was used to assess whether there was any significant difference between the models (significance threshold was set to p

Introduction Choroid plexus (CP) is a physiological barrier, producing cerebrospinal fluid (CSF), neurotrophic, and inflammatory factors. It’s also involved in the neuro-immune axis, facilitating the interplay between central and peripheral inflammation, allowing trafficking of immune cells. Coherently, CP enlargement has been found in psychiatric diseases characterized by inflammatory signature. Although CP volume correlates with central microglia activation in major depressive disorder (MDD), it’s never been directly associated with peripheral markers in mood disorders. Objectives Examine CP volume in mood disorders and healthy controls (HC) in relation to clinical features and peripheral inflammatory markers. Methods CP volume was extracted with FreeSurfer in 72 HC and 152 age- and sex-matched depressed patients: 79 BD and 73 MDD. Plasma analytes in patients were collected through immunoassay technology (Bioplex). We tested for the effect of age by group on CP volume. Then we focused on the interaction between illness duration and diagnosis in predicting CP volume. After testing the effect of specific analytes by diagnosis, we calculated moderated moderation models (SPSS, PROCESS) setting each analyte as independent variable, CP volume as predicted variable and illness duration and diagnosis as moderators. We get the effects’ significance with the likelihood ratio statistic, always controlling for age, sex, and intracranial volume. Results Patients were comparable in illness duration and severity. CP volume is differentially distributed through groups (right: p=0.04; left: p

Introduction Deficit in Theory of Mind (ToM) is a core feature of schizophrenia (SZ), while adverse childhood experiences (ACEs) can contribute to worsen ToM abilities through their effect on brain functioning, structure and connectivity. Objectives Here, we investigated the effects of ACEs on brain functional connectivity (FC) during an affective and cognitive ToM task (AToM, CToM) in healthy control (HC) and SZ, and whether FC can predict the performance at the ToM task and patients’ symptoms severity. Methods The sample included 26 HC and 33 SZ. In an fMRI session, participants performed a ToM task targeting affective and cognitive domains. Whole-brain FC patterns of local correlation (LC) and multivariate pattern analysis (MVPA) were extracted. The significant MVPA clusters were used as seeds in further seed-based connectivity analyses. Second-level analyses were modelled to investigate the interaction between ACEs, the diagnosis, and the task, corrected for age, sex, and equivalent doses of chlorpromazine (p

Introduction Previous findings show that the depressive state is characterized by a peculiar suppression of the resting state functional connectivity (rsFC) anti-correlation between resting-state networks (e.g., Default Mode Network) and task-positive networks (e.g., Sensory-Motor Network) in favor of an abnormal positive rsFC pattern. This suggests a large-scale functional disbalance in adaptively switching the attentional focus from an internal-oriented cognitive modality to an external-oriented processing modality. Yet, according to further evidence, such a functional inversion is primarily driven by the global signal (GS) (i.e., by an abnormal large-scale topographical reconfiguration) in major depressive disorder (MDD). However, it is not clear if similar alterations may affect bipolar disorder (BD) in depressive phase. Objectives Investigation of the global topography of the depressive syndrome as a potential transnosographic endophenotype and evaluation of the GS on generating differences between groups. Methods We compared large-scale rsFC patterns in a group of healthy controls (HC) (n=70) and a group of patients with BD (n=70) during a depressive episode. In order to investigate the impact of the GS, we further performed all analyses both with and without GS regression (GSR). Results Compared to HC, patients with an ongoing major depressive episode exhibit specific resting-state changes that are only observed when analysis is performed without regressing GS. Patients were found to exhibit an (i) abnormally strong GS contribution within an extended cluster comprising regions known to be part of highly interconnected hubs (i.e., transmodal networks) and showing functional relations’ core along the cortical midline and a (ii) diminished influence of the GS in correspondence of frontoparietal and occipitotemporal regions. Notably, no traces of such changes -differentiating the global topography of patients from HC- held when applying GSR. Conclusions Our results (i) suggest that rsFC alterations detected stem from a global rather than a local source and (ii) corroborate the impact GS can exert on generating within and between-networks differences. Hence, we underline the necessity that future investigations on groups with expected altered topographical distribution include GS within data-analysis and a proper evaluation of its involvement. Nonetheless, our results are in line with previous evidence of altered global topography in MDD. Hence, we interpreted this finding as a benchmark of a whole-brain functional disbalance toward self-oriented cognition characterizing the transnosographic depressive syndrome. Disclosure of Interest None Declared

Introduction Persisting and disabling depressive symptomatology represent a prominent feature of the post-acute COVID-19 syndrome. Sars-CoV-2-induced immune system dysregulation mainly result in a cytokine storm. Once in the brain, inflammatory mediators negatively affect neurotransmission, microglia activation, and oxidative stress, possibly disrupting critical brain neurocircuits which underpin depressive symptoms. So far, only inflammatory markers based on leukocyte counts have been linked to depressive outcome in COVID survivors. However, an accurate immune profile of post-COVID depression has yet to be elucidated. Objectives Identify inflammatory mediators that predict post-COVID depression among a panel of cytokines, chemokines, and growth factors, with a machine learning routine. Methods 88 COVID age- and sex-matched survivors’ (age 52.01 ± 9.32) were screened for depressive symptomatology one month after the virus clearance through the Beck Depression Inventory (BDI-13), with 12.5% of the individuals scoring in the clinical range (BDI-13 ≥ 9). Immune assay was performed through Luminex system on blood sampling obtained in the same context. We entered 42 analytes into an elastic net penalized regression model predicting presence of clinical depression, applied within a 5-fold nested cross-validation machine learning routine running in MATLAB. Significance of predictors was evaluated according to variable inclusion probability (VIP), as returned by 5000 bootstraps. Socio-demographics, previous psychiatric history, hospitalization, time after discharge were used as covariates. Results The model reached a balance accuracy of 73% and AUC of 77%, correctly identifying 73% of people suffering from clinically relevant depressive symptoms (Figure1). Depressive symptomatology was predicted by high levels of CCL17, ICAM-1, MIF, whereas CXCL13, CXCL12, CXCL10, CXCL5, CXCL2, CCL23, CCL15, CCL8, GM-CSF showed a protective effect (Figure2). Image: Image 2: Conclusions This is the first study highlighting a putative inflammatory signature of post-COVID depression. Consistently to the immune profile of Major Depressive disorder, upregulation of innate immunity mediators seems to foster depressive symptoms in the aftermath of COVID. Interestingly, recruiters of B and T cells promoting a physiological adaptive response to viral infection also mitigate its psychiatric sequelae. Understanding the biological basis of post-COVID depression could pave the way for personalized treatments capable of reducing its add-on burden. Disclosure of InterestNone Declared

Introduction Every year at least one million people die by suicide, with major depressive disorder (MDD) being one of the major causes of suicide deaths. Current suicide risk assessments rely on subjective information, are time consuming, low predictive, and poorly reliable. Thus, finding objective biomarkers of suicidality is crucial to move clinical practice towards a precision psychiatry framework, enhancing suicide risk detection and prevention for MDD. Objectives The present study aimed at applying machine learning (ML) algorithms on both grey matter and white-matter voxel-wise data to discriminate MDD suicide attempters (SA) from non-attempters (nSA). Methods 91 currently depressed MDD patients (24 SA, 67 nSA) underwent a structural MRI session. T1-weighted images and diffusion tensor imaging scans were respectively pre-processed using Computational Atlas Toolbox 12 (CAT12) and spatial tract-based statistics (TBSS) on FSL, to obtain both voxel-based morphometry (VBM) and fractional anisotropy (FA) measures. Three classification models were built, entering whole-brain VBM and FA maps alone into a Support Vector Machine (SVM) and combining both modalities into a Multiple Kernel Learning (MKL) algorithm. All models were trained through a 5-fold nested cross-validation with subsampling to calculate reliable estimates of balanced accuracy, specificity, sensitivity, and area under the receiver operator curve (AUC). Results Models’ performances are summarized in Table 1.Table 1. Models’ performances. Input features Algorithm Specificity Sensitivity Balanced accuracy AUC VBM SVM 55.00% 50.00% 52.50% 0.55 FA SVM 72.00% 54.00% 63.00% 0.62 VBM and FA MKL 68.00% 54.00% 61.00% 0.58 Abbreviations: AUC, area under the receiver operator curve; FA, fractional anisotropy; VBM, voxel-based morphometry. Conclusions Overall, although overcoming the random classification accuracy (i.e., 50%), performances of all models classifying SA and nSA MDD patients were moderate, possibly due to the imbalanced numerosity of classes, with SVM on FA reaching the highest accuracy. Thus, future studies may enlarge the sample and add different features (e.g., functional neuroimaging data) to develop an objective and reliable predictive model to assess and hence prevent suicide risk among MDD patients. Disclosure of Interest None Declared

Introduction One of the main obstacles in providing effective treatments for major depressive disorder (MDD) is clinical heterogeneity, whose neurobiological correlates are not clearly defined. A biologically meaningful stratification of depressed patients is needed to promote tailored diagnostic procedures. Objectives Using structural data, we performed an unsupervised clustering to define clinically meaningful clusters of depressed patients. Methods T1-weighted and diffusion tensor images were obtained from 102 MDD patients. In 64 patients, clinical symptoms, number of stressful life events, severity and exposure to adverse childhood experiences were evaluated using the Beck Depression Inventory (BDI), Schedule of Recent Experiences (SRE), Risky Family Questionnaire (RFQ), and Childhood Trauma Questionnaire (CTQ). Clustering analyses were performed with extracted tract-based fractional anisotropy (TBSS, FSL), cortical thickness, surface area, and regional measures of grey matter volumes (CAT12). Gaussian mixture model was implemented for clustering, considering Support Vector Machine (SVM) as classifier. A 10x2 repeated cross-validation with grid search was performed for hyperparameters tuning and clusters’ stability. The optimal number of clusters was determined by normalized stability, Akaike and Bayesian information criterion. Analyses were adjusted for total intracranial volume, age, and sex. The clinical relevance of the identified clusters was assessed through MANOVA, considering domains of clinical scales as dependent variables and clusters’ labels as fixed factors. Discriminant analysis was subsequently performed to assess the discriminative power of these variables. Results Cross-validated clustering approach identified 2 highly stable clusters (normalized stability=0.316, AIC=-80292.48, BIC=351329.16). MANOVA showed a significant between-clusters difference in clinical scales scores (p=0.038). Discriminant analysis distinguished the two clusters with an accuracy of 78.1%, with BDI behavioural and CTQ minimisation/denial domains showing the highest discriminant values (0.325 and 0.313). Conclusions Our results defined two biologically informed clusters of MDD patients associated with childhood trauma and specific clinical profiles, which may assist in targeting effective interventions and treatments. Disclosure of Interest None Declared

Introduction Major depressive disorder (MDD) is largely considered the most prevalent psychiatric disorder worldwide. Despite its domineering presence, effective treatment for many individuals remains elusive. Investigation into relevant biological markers, specifically neuroimaging correlates, of MDD and treatment response have gained traction in recent years; however, findings are still inconsistent. Objectives In this study, we aimed to investigate the resting state functional connectivity patterns associated with treatment response in MDD inpatients in a real world setting. Methods Forty-three inpatients suffering from a major depressive episode were recruited from the psychiatric ward at IRCCS San Raffaele Hospital in Milan, Italy. Symptom severity was assessed via the 21-item Hamilton Depression Rating Scale (HDRS). The percentage of decrease in HDRS scores from admission to discharge was then calculated with the formula [(HDRS admission – HDRS discharge) * 100] / HDRS admission. All patients underwent a 3T MRI scan within one week of admission to acquire resting-state fMRI images, which included 200 sequential T2*-weighted volumes. Images were preprocessed using the CONN toolbox, running within Statistical Parametric Mapping (SPM 12). Preprocessing was performed according to a standard pipeline. A voxel-wise metric, intrinsic connectivity contrast (ICC), was implemented to explore the global resting state functional connectivity (rs-FC) patterns associated with treatment response. ICC-derived maps were then entered in the second-level analyses to examine the effect of the percentage of HDRS decrease, including age, sex, admission HDRS score, duration of hospitalization, and antidepressant dose equivalents as nuisance covariates. Results We found that the percentage of HDRS decrease after treatment predicted rs-FC. ICC analysis identified 2 clusters where changes in HDRS scores were significantly associated with rs-FC, with increased connectivity in the supramarginal gyrus (pFDR = 0.002) and decreased connectivity in the amygdala and parahippocampal gyrus (pFDR = 0.047). Conclusions Our results suggest that altered connectivity of the supramarginal gyrus, amygdala and parahippocampal gyrus is related to antidepressant treatment response. Given that these brain areas are implicated in emotional processing and mood, it is conceivable that a better integrity of brain connectivity may facilitate treatment response in major depression. Disclosure of Interest None Declared

Introduction The role of the interaction between the serotonin-transporter-linked promoter region (5HTTLPR) and stressful condition in determining the vulnerability to depression has been widely investigated. Nevertheless, empirical research provides contrasting findings. Recently, the differential susceptibility to environment model proposed a conceptual shift respect to the classical interpretation of 5-HTTLPR: viewing the short (s) and the long (l) allele not as associated to different traits of vulnerability (respectively vulnerable or not), but determining different plasticity levels (respectively, more and less plasticity) and, thus, different susceptibilities to the environment (respectively, high and low susceptibility). Objectives As 5-HTTLPR is involved in plasticity, the main goal of the present study is to demonstrate that the interaction between the polymorphism and stress emerges when assessing its effects according to temporal factors in a dynamic process perspective. Methods We explored our hypothesis, exploiting a meta analytic approach. We searched PubMed, PsychoINFO, Scopus and EMBASE databases and 1096 studies were identified and screened, resulting in 22 studies to be included in the meta-analyses. We applied the DerSimonian and Laird random-effects model to estimate crude odds ratios for risk of depression according to 5HTTLPR and we assessed heterogeneity using the I² and Cochran’s Q statistic. We stratified the staties according to (i) stress duration (i.e., chronic vs. acute stress) and (ii) time elapsed between the end of the stressful condition and the assessment of depression (i.e., within one year vs. more than one year). Results When stratifying for the duration of stress, the effect of the 5-HTTLPR x stress interaction emerged only in the case of chronic stress (OR 1.43, 95%IC 1.16-1.77, I²= 52%, Q=25.25; Figure 1), with a significant subgroup difference (p=0.004). The stratification according to time interval revealed a significant interaction only for intervals within one year (OR 1.23, 95%IC 1.03-1-46, I²= 67%, Q=39.35), though no difference between subgroups was found. The critical role of time interval clearly emerged when considering only chronic stress: a significant effect of the 5-HTTLPR and stress interaction was confirmed exclusively within one year (OR 1.53, 95%IC 1.17-2.02, I²= 45%, Q=10.94; Figure 2) and a significant subgroup difference was found (p=0.01). Image: Image 2: Conclusions Our results show that the 5-HTTLPR x stress interaction is a dynamic process, producing different effects at different time-points, and indirectly confirm that s-allele carriers are both at higher risk and more capable to recover from depression. Overall, these findings expand the current view of the interplay between 5-HTTLPR and stress adding the temporal dimension, resulting in a three-way interaction: gene x environment x time. Disclosure of Interest None Declared

Introduction Many different long-term neuropsychiatric sequelae of the novel Coronavirus have been described after the pandemic outbreak. One of the most common symptoms in the months following infection is related to “brain fog”. This condition includes several signs of cognitive impairment like mental slowness, deficits in attention, executive functions, processing, memory, learning, and/or psychomotor coordination, which can be perceived on a subjective level and further confirmed by objective data. Since this kind of mental status has been documented in previous viral infections, and the SARS-COV-2 has been characterized by a worldwide diffusion, investigation into this condition in post-covid individuals is warranted. Currently, several hypotheses on its pathophysiology have been put forward, mostly hypothesizing a direct effect of the virus on the central nervous system or indirect consequences of the inflammatory response. Objectives The aim of our research is to analyze brain correlates of subjective cognitive complaints in Covid-19 survivors using multimodal brain imaging. Methods We performed a voxel-based morphometry (VBM) and a resting state functional connectivity analysis on 60 post-COVID-19 individuals recruited from the San Raffaele Hospital in Milan, that underwent a 3 tesla MRI scan. We assessed the perceived cognitive impairment both after the infection and at the time of the MRI scan through the PROMIS Cognitive Abilities scale. The difference of the two scores (delta PROMIS) was calculated as a measure of cognitive improvement over time. Results We found the perceived amelioration of cognitive abilities (delta PROMIS) to be positively associated to grey matter volumes in the bilateral caudate, putamen and pallidum (pFWE: ˂0.001). Moreover, in the resting state fMRI analysis, subjective cognitive status at MRI was found to be associated with functional connectivity between the right putamen and pallidum, and two clusters belonging to the attentional (pFWE: ˂0.001) and salience (pFWE: 0.02) networks. Conclusions This is one of the first studies investigating brain correlates of subjective cognitive impairment after COVID-19 infection; our main finding is the convergence of structural and functional results on brain areas located within the basal ganglia, implying their possible role in the pathophysiology of the condition. Moreover, this research could be interpreted as the first step toward understanding a very complex condition, with potential implications for the development of treatment and neurorehabilitative strategies. Disclosure of InterestNone Declared

Introduction Bipolar patients (BP) frequently have cognitive deficits, that impact on prognosis and quality of life. Finding biomarkers for this condition is essential to improve patients’ healthcare. Given the association between cognitive dysfunctions and structural brain abnormalities, we used a machine learning approach to identify patients with cognitive deficits. Objectives The aim of this study was to assess if structural neuroimaging data could identify patients with cognitive impairments in several domains using a machine learning framework. Methods Diffusion tensor imaging and T1-weighted images of 150 BP were acquired and both grey matter voxel-based morphometry (VBM) and tract-based white matter fractional anisotropy (FA) measures were extracted. Support vector machine (SVM) models were trained through a 10-fold nested cross-validation with subsampling. VBM and FA maps were entered separately and in combination as input features to discriminate BP with and without deficits in six cognitive domains, assessed through the Brief Assessment of Cognition in Schizophrenia. Results The best classification performance for each cognitive domain is illustrated in Table 1. FA was the most relevant neuroimaging modality for the prediction of verbal memory, verbal fluency, and executive functions deficits, whereas VBM was more predictive for working memory and motor speed domains.Table 1. Performance of best classification models. Input feature Balance Accuracy (%) Specificity (%) Sensitivity (%) Verbal Memory FA 60.17 51.31 43 Verbal Fluency FA 57.67 62 53.33 Executive functions FA 60 63.33 56.67 Working Memory VBM 56.50 56 57 Motor speed VBM 53.50 47.67 59.33 Attention and processing speed VBM + FA 58.33 49.17 67.5 Conclusions Overall, the tested SVM models showed a good predictive performance. Although only partially, our results suggest that different structural neuroimaging data can predict cognitive deficits in BP with accuracy higher than chance level. Unexpectedly, only for the attention and processing speed domain the best model was obtained combining the structural features. Future research may promote data fusion methods to develop better predictive models. Disclosure of Interest None Declared

Abstract Bipolar disorder (BD) has been consistently associated with alterations in the immune system. Evidence suggests a condition of systemic low-grade inflammation due to decreased adaptive, increased innate immunity, with higher levels of circulating cytokines, higher macrophage/monocyte inflammatory activation patterns, and higher neutrophils to lymphocyte counts; and with a dynamic pattern of premature immunosenescence and partial T cell defect starting early in adolescence, involving a reduction of naïve T cells and an expansion of memory and senescent T cells. Quantitative analysis of circulating inflammatory markers suggested persistent low-grade inflammation. A growing literature suggests that the immune system plays a core role in maintaining brain homeostasis, with both adaptive and innate immune support, ensured by cell trafficking across the blood brain barrier, being essential for brain maintenance and repair in healthy conditions, and disrupted in brain disorders including BD. Measured in peripheral blood, these markers of altered immuno-inflammatory setpoints parallel activation of microglia and disruption of white matter (WM) integrity in the brain. Studies in the field are in its infancy, but findings by our group showed that: circulating Th17 cells correlated with higher FA, while regulatory FOXP3+ cells correlated with higher RD and MD, and with lower fMRI neural responses in the right dorsolateral prefrontal cortex; higher circulating cytokine-producing NK cells were fostered by ongoing lithium treatment and directly correlated with better FA, and inversely with RD and MD, also partially mediating the known benefits from lithium on WM; and activation status and expression of killer proteins by cytotoxic CD8+ T cells negatively associated with WM microstructure, thus suggesting that CD8+ T cells can leave the blood stream to migrate into the brain and induce an immune-related WM damage in BD. Implications of these findings for neuroprogression, clinical outcomes, and new treatment strategies of the disorder will be discussed. Disclosure of Interest None Declared

Introduction In recent years much focus has been put on the role of immune/inflammatory alterations in affecting Major Depression (MDD) development and antidepressant efficacy. Neutrophil-to-lymphocyte ratio (NLR) is an inexpensive inflammatory marker shown to be elevated in depressed patients, with large population studies reporting this effect only in women. However, its relation to treatment response is much less clear. Reduced hippocampal volumes (HV) are among the few consistent brain structural predictors of poor treatment response, and they have been shown to be influenced by inflammatory status. Objectives To investigate the effect of NLR on treatment response in MDD patients, testing a possible moderating role of sex. To investigate the effect of NLR on HV and test a possible mediating role of the latter in the relation between NLR and treatment response. Methods Our study was performed on a sample of 120 MDD inpatients suffering from a non psychotic depressive episode (F=78; M=42). Depression severity was assessed via the Hamilton Depression Rating Scale (HDRS), both at admission and discharge; as a measure of treatment response, delta HDRS was calculated subtracting the two scores. NLR was calculated for each subject. Patients underwent 3T MRI acquisition and bilateral HV were estimated. Results We found a significant moderating effect of sex on the relationship between NLR and Delta HDRS (p < 0.001): a negative relation was found in women (p < 0.001) and a positive one in men (p = 0.042). NLR was found to negatively affect left HV in the whole sample (p = 0.027) and in women (p = 0.038). A positive effect on Delta HDRS was found for both left (p = 0.038) and right (p = 0.027) HV. Finally, we found a significant indirect effect of NLR values on Delta HDRS through left HV in women (95% BCa CI [- 0.948, -0.017]); the direct effect of NLR on Delta HDRS also remained significant (p = 0.002). Conclusions Sex was found to moderate the relation between NLR and treatment response. The detrimental effect in women is in line with previous reports linking inflammation to hampered antidepressant effect; the positive one in men is more surprising: however, the only studies to date on the effect of NLR on antidepressant efficacy report a positive effect in patients with psychotic depression. In women we found NLR to affect treatment response partially through its effect on left HV, providing a possible, albeit incomplete, mechanistic explanation of the effect of inflammatory status on antidepressant efficacy. Disclosure of Interest None Declared

Introduction The new coronavirus disease (COVID-19) has important physical and mental health implications at short and long term. Some inflammatory parameters are implicated in the maintenance of psychiatric symptoms, especially those of anxiety and depression. Additionally, growing literature attributes a role to interoception in several mental health conditions. Objectives We investigated the involvement of the interoception in COVID-19 survivors and its possible associations with psychopathological and inflammatory variables. Methods Our study included 57 people surviving COVID-19 at one month follow-up after recovery. Individual interoceptive accuracy (IA) measure was obtained through heart-beat perception task. A measure of accuracy in external time perception (TA) was also obtained asking people to mentally produce a duration of 10s. Each participant completed State-Trait Anxiety Inventory - STAI-Y; Zung Self-Rating Depression Scale - ZSDS; Beck Depression Inventory - BDI-II; Impact of Events Scale - IES-R and Multidimensional Assessment of Interoceptive Awareness - MAIA. Peripheral inflammation markers were obtained in a subsample of 40 people by a blood sampling conducted at the time of admission and discharge from hospital. Correlation, regression and GLM analyses were performed with SPSS. Mediation analysis were performed with Hayes’ Process tool. Results TA is not associated with IA, symptomatological measures and bodily awareness. Trusting is the only aspect of body awareness associated with IA (p=.021). Noticing (p=.010), Not-distracting (p=.009), Not-worrying (p=.012) and Trusting (p=.001) predict anxiety psychopathology. Poor IA predict anxiety symptomatology (p=.004) and part of this effect is mediated by Trusting [Fig.1]. In the end, platelets count at the time of hospitalization negatively correlates with anxiety symptoms (p=.003). Image: Conclusions COVID-19 hospitalization could be considered a psychophysical traumatic experience which involved mental and physical health and the connection and integration between them. It’s necessary to deepen the different facets of body awareness and IA in post-covid stages and to study how interoceptive dimensions change over time. Further research is needed to investigate the specific role of platelets in prominent anxiety psychopathology detected in COVID-19 survivors, wondering about their possible involvement in the dysfunctional interoception process too. Disclosure of Interest None Declared

Introduction Major depressive disorder (MDD) often involves immune dysregulation with high peripheral levels of pro-inflammatory cytokines that might have an impact on the clinical course and treatment response. Moreover, MDD patients show brain volume changes and white matter (WM) alterations that are already existing in the early stage of illness. Objectives The aim of the present review is to elucidate the association between inflammation and WM integrity and its impact on the pathophysiology and progression of MDD as well as the role of possible novel biomarkers of treatment response to improve MDD prevention and treatment strategies. Methods We conducted an electronic literature search of PubMed on studies that examined the role of inflammation in depression and that focused on WM integrity and pro-inflammatory cytokines as predictors of antidepressant response. Results There is evidence for central effects of peripheral inflammation which could activate microglia which, in turn, might trigger a cascade of inflammatory processes leading to neurotransmitter imbalances. Numerous studies indicated that both altered levels of peripheral inflammatory markers, particularly TNF-α, IL-6, and CRP as well as WM integrity might predict antidepressant treatment outcome. Conclusions Despite mounting evidence on the impact of the immune system on WM microstructure, no study has yet addressed the interaction between the two factors in influencing antidepressant response. There is a lack of reproducible biomarkers predicting treatment response on an individual basis. The availability of such biomarkers would enable more efficient and personalized treatments with a faster treatment response and better prevention of treatment resistance. Disclosure No significant relationships.

Abstract Background In recent years, numerous studies have highlighted the overlap between autism spectrum disorder (ASD) and catatonia, both from a clinical and pathophysiological perspective. This study aimed to investigate the relationship between the autism spectrum (autistic traits and ASD signs, symptoms, and behavioral manifestation) and Catatonia Spectrum (CS). Methods A total sample of 376 subjects was distributed in four diagnostic groups. Subjects were assessed with the Structured Clinical Interview for DSM-5, Research Version, the Adult Autism Subthreshold Spectrum (AdAS Spectrum), and CS. In the statistical analyses, the total sample was also divided into three groups according to the degree of autism severity, based on the AdAS Spectrum total score. Results A statistically significant positive correlation was found between AdAS Spectrum and CS total score within the total sample, the gender subgroups, and the diagnostic categories. The AdAS Spectrum domains found to be significantly and strongly correlated with the total CS score were hyper–hypo reactivity to sensory input, verbal communication, nonverbal communication, restricted interests and rumination, and inflexibility and adherence to routine. The three groups of different autistic severity were found to be distributed across all diagnostic groups and the CS score increased significantly from the group without autistic traits to the group with ASD. Conclusions Our study reports a strong correlation between autism spectrum and CS.

Changes in chicken embryo thymus after partial decerebration (including the hypophysis) and after hypophyseal or thymic allograft were investigated. Chicken embryos were partially decerebrated at 36-40 hr of incubation and on day 12 received a hypophysis or a thymus allograft from 18-day-old donor embryos. The thymuses of normal, sham-operated and partially decerebrate embryos were collected on day 12 and 18. The thymuses of the grafted embryos were collected on day 18. The samples were examined with histological method and tested for the anti-PCNA and anti-CD3 immune-reactions. After partial decerebration, the thymic cortical and medullary compartments diminished markedly in size. Anti-PCNA and anti-CD3 revealed a reduced immune-reaction, verified also by statistical analysis. In hypophyseal or grafted embryos, the thymic morphological compartments improved, the anti-PCNA and anti-CD3 immune-reactions recovered much better after the thymic graft, probably due to the thymic growth factors and also by an emigration of thymocytes from the same grafted thymus.