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
Bipolar disorder (BD) is associated with adverse childhood experiences (ACE), which worsen the lifetime course of illness, and with signs of widespread disruption of white matter (WM) integrity in ...adult life. ACE are associated with changes in WM microstructure in healthy humans.
We tested the effects of ACE on diffusion-tensor imaging (DTI) measures of WM integrity in 80 in-patients affected by a major depressive episode in the course of BD. We used whole-brain tract-based spatial statistics in the WM skeleton with threshold-free cluster enhancement of DTI measures of WM microstructure: axial, radial and mean diffusivity, and fractional anisotropy.
ACE hastened the onset of illness. We observed an inverse correlation between the severity of ACE and DTI measures of axial diffusivity in several WM fibre tracts contributing to the functional integrity of the brain and including the corona radiata, thalamic radiations, corpus callosum, cingulum bundle, superior longitudinal fasciculus, inferior fronto-occipital fasciculus and uncinate fasciculus.
Axial diffusivity reflects the integrity of axons and myelin sheaths, and correlates with functional connectivity and with higher-order abilities such as reasoning and experience of emotions. In patients with BD axial diffusivity is increased by lithium treatment. ACE might contribute to BD pathophysiology by hampering structural connectivity in critical cortico-limbic networks.
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
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
Bipolar disorder (BD) is associated with signs of widespread disruption of white matter (WM) integrity. A polymorphism in the promoter of the serotonin transporter (5‐HTTLPR) influenced functional ...cortico‐limbic connectivity in healthy subjects and course of illness in BD, with the short (s) allele being associated with lower functional connectivity, and with earlier onset of illness and poor response to treatment. We tested the effects of 5‐HTTLPR on diffusion tensor imaging (DTI) measures of WM microstructure in 140 inpatients, affected by a major depressive episode in course of BD, of Italian descent. We used whole brain tract‐based spatial statistics in the WM skeleton with threshold‐free cluster enhancement of DTI measures of WM microstructure: axial, radial and mean diffusivity and fractional anisotropy. Compared with l/l homozygotes, 5‐HTTLPR*s carriers showed significantly increased radial and mean diffusivity in several brain WM tracts, including corpus callosum, cingulum bundle, uncinate fasciculus, corona radiata, thalamic radiation, inferior and superior longitudinal fasciculus and inferior fronto‐occipital fasciculus. An increase of mean and radial diffusivity, perpendicular to the main axis of the WM tract, is thought to signify increased space between fibers, thus suggesting demyelination or dysmyelination, or loss of bundle coherence. The effects of 5‐HTTLPR on the anomalous emotional processing in BD might be mediated by changes of WM microstructure in key WM tracts contributing to the functional integrity of the brain.
White matter microstructure in bipolar disorder is influenced by a serotonin transporter gene polymorphism
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.
