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.
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
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 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) 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
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 Interest
None Declared
Theory of Mind, the ability to understand the potential mental states and intentions of others, represents a relevant aspect of social cognition, with high impact on the capacity to interact within ...the social world. This very human ability has been one of the focuses of neuroscience research in the past decades and data from neuroimaging studies allowed to identify a Theory of Mind network and to formulate a neurobiological model. Concurrent neuropsychiatric studies showed that Theory of Mind is differently impaired in several conditions, among these, in schizophrenia, a disease characterized by functional and social disability. This paper addresses the issue of neurofunctional correlates of Theory of Mind deficits in schizophrenia, reviewing functional imaging studies of the past ten years comparing schizophrenia patients to healthy controls. Several differences in hemodynamic response between patients and controls were observed in the areas known to be critically involved in social cognition, such as the medial prefrontal cortex, temporal cortex surrounding superior temporal sulcus and temporo-parietal junction and cingulate cortex. Results are promising, however they are still heterogeneous. The reported variability could depend on factors related to the construct of Theory of Mind itself, technical aspects and psychopathological/physiopathological mechanisms and needs to be further addressed by future studies.
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
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<0.05 FWE). FC values significantly affected by ACEs (Risky Family Questionnaire) were entered in a cross-validated LASSO regression predicting symptoms severity (Positive and Negative Syndrome Scale, PANSS) and task performance measures (accuracy and response time).
Results
In AToM, LC showed significant different effects of ACE between HC and SZ in frontal pole, caudate and cerebellum. MVPA showed significant widespread interaction in cortico-limbic regions, including prefrontal cortex, precuneus, insula, parahippocampus, cingulate cortex, temporal pole, thalamus, and cerebellum in AToM and CToM. SBC analyses found significant target regions in the frontal pole, cerebellum, pre and postcentral gyrus, precuneus, lateral occipital cortex, angular gyrus, and paracingulate gyrus. LASSO regression predicted PANSS score (R
2
=0.49) and AToM response latency time (R
2
=0.37).
Conclusions
Our findings highlighted a widespread different effect of ACEs on brain FC in ToM networks in HC and SZ. Notably, the FC in these regions is predictive of behavioral ToM performance and clinical outcomes.
Disclosure of Interest
None Declared
