19 results on '"Calesella F"'
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2. Putative immune-inflammatory signature of post-Covid-19 depression: a longitudinal study
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. Identifying suicide attempters among bipolar depressed patients using structural neuroimaging: a machine learning study
13. Prediction of cognitive impairment in mood disorders using multimodal structural neuroimaging: a machine learning study
14. Immune-inflammation and structural neuroimaging differentiate bipolar and unipolar depression: a machine learning study
15. A machine learning pipeline for efficient differentiation between depressed bipolar disorder and major depressive disorder patients based on structural neuroimaging
16. Data-driven stratification of depressed patients based on structural neuroimaging signatures: a stability-based relative clustering validation approach
17. Immune-inflammation and structural neuroimaging differentiate bipolar and unipolar depression: a machine learning study
18. P.0689 Reduced cortico-limbic habituation identifies bipolar depressed suicide attempers: a machine learning study
19. P.0088 Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis
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