34 results on '"Calesella F"'
Search Results
2. Unsupervised neurobiology-driven stratification of clinical heterogeneity in depression
3. Predicting Suicide Attempts among Major Depressive Disorder Patients with Structural Neuroimaging: A Machine Learning Approach
4. Predicting unipolar and bipolar depression using inflammatory markers, neuroimaging and neuropsychological data: a machine learning study
5. The different effect of adverse childhood experiences on Theory of Mind brain networks in schizophrenia and healthy controls
6. Cognitive distortions and structural neuroimaging data predict depression severity in unipolar and bipolar depression: a machine learning study
7. Unsupervised neurobiologically-driven stratification of clinical heterogeneity in treatment-resistant depression
8. ADHD levels and resting-state functional connectivity patterns in predominantly-cocaine versus other-substances abusers
9. Putative immune-inflammatory signature of post-Covid-19 depression: a longitudinal study
10. Functional neuroimaging for the differentiation between healthy controls, depressed bipolar and major depressive patients: a machine learning study
11. Combining clinical data, genetics, and adverse childhood experiences for suicidality prediction in mood disorders: a machine learning approach
12. Predicting cognitive impairment in depression: a machine learning approach on multimodal structural neuroimaging
13. Moving beyond clinical approaches: machine learning on neuroimaging and cognitive features for the differential diagnosis between unipolar and bipolar depression
14. The effect of adverse childhood experiences on theory of mind brain networks: association with clinical and behavioural outcomes in schizophrenia
15. Machine learning signature in differentiating bipolar and unipolar depression with multimodal structural neuroimaging data and neuropsychology
16. Classification of bipolar disorder from multi-site regional-based cortical morphology features using support vector machine technique
17. Identifying suicide attempters among bipolar depressed patients using structural neuroimaging: a machine learning study
18. Prediction of cognitive impairment in mood disorders using multimodal structural neuroimaging: a machine learning study
19. Immune-inflammation and structural neuroimaging differentiate bipolar and unipolar depression: a machine learning study
20. A machine learning pipeline for efficient differentiation between depressed bipolar disorder and major depressive disorder patients based on structural neuroimaging
21. Data-driven stratification of depressed patients based on structural neuroimaging signatures: a stability-based relative clustering validation approach
22. P.0088 Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis
23. P.0689 Reduced cortico-limbic habituation identifies bipolar depressed suicide attempers: a machine learning study
24. One-year mental health outcomes in a cohort of COVID-19 survivors
25. Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance.
26. Inflammatory Markers Predict Blood Neurofilament Light Chain Levels in Acute COVID-19 Patients.
27. Adverse childhood experiences differently affect Theory of Mind brain networks in schizophrenia and healthy controls.
28. Choroid plexus volume is increased in mood disorders and associates with circulating inflammatory cytokines.
29. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.
30. Correction: The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.
31. Reduced corticolimbic habituation to negative stimuli characterizes bipolar depressed suicide attempters.
32. Insulin resistance disrupts white matter microstructure and amplitude of functional spontaneous activity in bipolar disorder.
33. Long-term effect of childhood trauma: Role of inflammation and white matter in mood disorders.
34. Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis.
Catalog
Books, media, physical & digital resources
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.