45 results on '"Vai B"'
Search Results
2. A topological data analysis approach for multimodal feature-driven stratification of major depressive disorder patients by treatment resistant depression
3. Cognitive distortions and structural neuroimaging data predict depression severity in unipolar and bipolar depression: a machine learning study
4. Moving beyond clinical approaches: machine learning on neuroimaging and cognitive features for the differential diagnosis between unipolar and bipolar depression
5. Combining clinical data, genetics, and adverse childhood experiences for suicidality prediction in mood disorders: a machine learning approach
6. Predicting cognitive impairment in depression: a machine learning approach on multimodal structural neuroimaging
7. Unsupervised neurobiologically-driven stratification of clinical heterogeneity in treatment-resistant depression
8. Functional neuroimaging for the differentiation between healthy controls, depressed bipolar and major depressive patients: a machine learning study
9. ADHD levels and resting-state functional connectivity patterns in predominantly-cocaine versus other-substances abusers
10. Machine learning signature in differentiating bipolar and unipolar depression with multimodal structural neuroimaging data and neuropsychology
11. Classification of bipolar disorder from multi-site regional-based cortical morphology features using support vector machine technique
12. Time moderates the interplay between 5-HTTLPR and stress on depression risk – Gene x environment interaction as dynamic process
13. Identifying suicide attempters among bipolar depressed patients using structural neuroimaging: a machine learning study
14. Prediction of cognitive impairment in mood disorders using multimodal structural neuroimaging: a machine learning study
15. Immune-inflammation and structural neuroimaging differentiate bipolar and unipolar depression: a machine learning study
16. A machine learning pipeline for efficient differentiation between depressed bipolar disorder and major depressive disorder patients based on structural neuroimaging
17. Data-driven stratification of depressed patients based on structural neuroimaging signatures: a stability-based relative clustering validation approach
18. P.0689 Reduced cortico-limbic habituation identifies bipolar depressed suicide attempers: a machine learning study
19. P.0279 Neural underpinnings of depressive and post-traumatic symptomatology in covid-19 survivors: a voxel-based morphometry study
20. P.0088 Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis
21. P.253 Immunoassay quantification and neuroimaging data for differentiating bipolar and unipolar depression: support vector machine on kernels and elastic net approaches
22. P.201 Higher baseline interleukin 13 marks poor antidepressant response to sleep deprivation and light therapy in bipolar disorder
23. P.141 Topological data analysis for genetic-driven stratification of patients with major depressive disorder
24. P82 Neurobiologic and clinical correlates of low-frequency TMS over the left DorsoLateral Prefrontal Cortex in Manic Episode
25. P.305 Inflammation and neuroimaging differentiate bipolar and unipolar depression: a multiple kernel learning approach
26. P.651 Mood congruent helplessness associates with prefrontal Intrinsic connectivity in major depression
27. P.630 Amygdala functional connectivity in depressed bipolar patients as possible predictor of antidepressant response to chronobiological treatment
28. P.061 Diffusion tensor imaging as a biomarker for the discrimination of bipolar and unipolar depression
29. P.029 Classifying mood disorders using multiple kernel learning on multimodal neuroimaging data: translating biological data into a diagnostic tool for depression
30. P.4.12 Classification of mood disorders using a multiple kernel approach on multimodal neuroimaging data
31. P.4.09 Corticolimbic connectivity mediates the early stress - suicidality relation in 5-HTTLPR S-carrier bipolar depression
32. P.2.d.011 - Fronto-limbic connectivity during emotional processing in bipolar depression: the role of 5-HT1A promoter polymorphism
33. P.1.i.029 - Tryptophan catabolism affects the neural correlates of mood-congruent processing biases in bipolar depression
34. P.1.i.033 - Excitatory amino acid transporters 1 affects corticolimbic circuitry during implicit processing of negative emotional stimuli in bipolar disorder
35. P.1.i.037 - Catechol-O-methyltransferase (COMT) polymorphism affects fronto-limbic connectivity during emotional processing in bipolar depression
36. P.2.f.017 Fronto-limbic connectivity in depression predicts response to SSRI administration
37. P.2.d.030 Clock genes associate with white matter integrity in depressed bipolar patients: a tract-based spatial statistics study
38. P.2.d.023 Effects of the interaction between lithium and glycogen synthase kinase 3 in effective connectivity on neural response to emotional processing in bipolar disorder
39. P.1.i.046 Influence of adverse childhood experience on gray matter volume in major psychosis compared to healthy controls
40. P.3.b.013 Disrupted effective connectivity of emotional circuitry in schizophrenia: a dynamic causal modeling study
41. P.2.d.037 Serotonin transporter promoter gene and white matter integrity: a tract-based spatial statistics study in bipolar patients
42. P.2.d.030 Effect of total sleep deprivation and light therapy on cortico–limbic connectivity in bipolar depression: a dynamic causal modeling study
43. P.2.d.011 Fronto–limbic disconnection in bipolar disorder
44. S.07.08 Disrupted effective connectivity of emotional circuitry in schizophrenia: a dynamic causal modeling study
45. P.8.b.011 Cortico-limbic dysfunction in borderline personality disorder: adverse childhood experiences and treatment with clozapine
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