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28 results on '"Calesella, Federico"'

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1. Correction: The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

2. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

3. Multi-Scale Assessment of Harmonization Efficacy on Resting-State Functional Connectivity

4. Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance

10. One-year mental health outcomes in a cohort of COVID-19 survivors

11. A Systematic Assessment of Feature Extraction Methods for Robust Prediction of Neuropsychological Scores from Functional Connectivity Data

16. Inflammatory Markers Predict Blood Neurofilament Light Chain Levels in Acute COVID-19 Patients.

18. A machine learning pipeline for efficient differentiation between bipolar and major depressive disorder based on multimodal structural neuroimaging

20. Multimodal structural neuroimaging data unveil data-driven subtypes of treatment-resistant depression.

21. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

22. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

23. Long-term effect of childhood trauma: Role of inflammation and white matter in mood disorders

25. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

26. One-year mental health outcomes in a cohort of COVID-19 survivors

27. Insulin resistance disrupts white matter microstructure and amplitude of functional spontaneous activity in bipolar disorder.

28. A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach

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