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
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
IntroductionBipolar 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.ObjectivesThe 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.MethodsDiffusion 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.ResultsThe 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 featureBalance Accuracy (%)Specificity (%)Sensitivity (%)Verbal MemoryFA60.1751.3143Verbal FluencyFA57.676253.33Executive functionsFA6063.3356.67Working MemoryVBM56.505657Motor speedVBM53.5047.6759.33Attention and processing speedVBM + FA58.3349.1767.5ConclusionsOverall, 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 InterestNone Declared
Bipolar disorder (BD) is a severe, disabling and life-threatening illness. Disturbances in emotion and affective processing are core features of the disorder with affective instability being ...paralleled by mood-congruent biases in information processing that influence evaluative processes and social judgment. Several lines of evidence, coming from neuropsychological and imaging studies, suggest that disrupted neural connectivity could play a role in the mechanistic explanation of these cognitive and emotional symptoms. The aim of the present study is to investigate the effective connectivity in a sample of bipolar patients.
Dynamic causal modeling (DCM) technique was used to study 52 inpatients affected by bipolar disorders consecutively admitted to San Raffaele hospital in Milano and forty healthy subjects. A face-matching task was used as activation paradigm.
Patients with BD showed a significantly reduced endogenous connectivity in the DLPFC to Amy connection. There was no significant group effect upon the endogenous connection from Amy to ACC, from ACC to Amy and from DLPFC to ACC.
Both DLPFC and ACC are part of a network implicated in emotion regulation and share strong reciprocal connections with the amygdale. The pattern of abnormal or reduced connectivity between DLPFC and amygdala may reflect abnormal modulation of mood and emotion typical of bipolar patients.
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
Catechol-O-methyltransferase (COMT) inactivates catecholamines, Val/Val genotype was associated to an increased amygdala (Amy) response to negative stimuli and can influence the symptoms severity and ...the outcome of bipolar disorder, probably mediated by the COMT polymorphism (rs4680) interaction between cortical and subcortical dopaminergic neurotransmission. The aim of this study is to explore how rs4680 and implicit emotional processing of negative emotional stimuli could interact in affecting the Amy connectivity in bipolar depression. Forty-five BD patients (34 Met carriers vs. 11 Val/Val) underwent fMRI scanning during implicit processing of fearful and angry faces. We explore the effect of rs4680 on the strength of functional connectivity from the amygdalae to whole brain. Val/Val and Met carriers significantly differed for the connectivity between Amy and dorsolateral prefrontal cortex (DLPFC) and supramarginal gyrus. Val/Val patients showed a significant positive connectivity for all of these areas, where Met carriers presented a significant negative one for the connection between DLPFC and Amy. Our findings reveal a COMT genotype-dependent difference in corticolimbic connectivity during affective regulation, possibly identifying a neurobiological underpinning of clinical and prognostic outcome of BD. Specifically, a worse antidepressant recovery and clinical outcome previously detected in Val/Val patients could be associated to a specific increased sensitivity to negative emotional stimuli.
Impaired emotional processing is a core feature of schizophrenia (SZ). Consistent findings suggested that abnormal emotional processing in SZ could be paralleled by a disrupted functional and ...structural integrity within the fronto-limbic circuitry. The effective connectivity of emotional circuitry in SZ has never been explored in terms of causal relationship between brain regions. We used functional magnetic resonance imaging and Dynamic Causal Modeling (DCM) to characterize effective connectivity during implicit processing of affective stimuli in SZ.
We performed DCM to model connectivity between amygdala (Amy), dorsolateral prefrontal cortex (DLPFC), ventral prefrontal cortex (VPFC), fusiform gyrus (FG) and visual cortex (VC) in 25 patients with SZ and 29 HC. Bayesian Model Selection and average were performed to determine the optimal structural model and its parameters.
Analyses revealed that patients with SZ are characterized by a significant reduced top-down endogenous connectivity from DLPFC to Amy, an increased connectivity from Amy to VPFC and a decreased driving input to Amy of affective stimuli compared to HC. Furthermore, DLPFC to Amy connection in patients significantly influenced the severity of psychopathology as rated on Positive and Negative Syndrome Scale.
Results suggest a functional disconnection in brain network that contributes to the symptomatic outcome of the disorder. Our findings support the study of effective connectivity within cortico-limbic structures as a marker of severity and treatment efficacy in SZ.
