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2026-11-12 11:31 |
[FZJ-2026-05924]
Journal Article
Hettwer, M. ; Saberi, A. ; Shafiei, G. ; et al
340. Common Cortical Patterns Shape Variability Within and Overlap Across Psychiatric Disorders – A Transdiagnostic ENIGMA Study
BackgroundMental disorders show pronounced phenotypic heterogeneity within diagnoses and overlap across them, limiting the ability of group-level studies to identify distinct brain correlates. Large, transdiagnostic samples and individualized approaches are therefore needed to reveal systematic patterns that underlie variability and overlap in psychiatric brain alterations.MethodsIn a global collaborative effort, we analyzed region-wise, in vivo cortical thickness (CT) estimates from 10,188 patients (schizophrenia, generalized anxiety, autism spectrum, bipolar, obsessive-compulsive, and major depressive disorders) and 12,251 controls (5-80 years old) [...]
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2026-11-12 11:24 |
[FZJ-2026-05921]
Journal Article
Liu, X. ; Liu, Z. ; Wan, B. ; et al
Episode-state-associated and shared intrinsic functional network patterns in bipolar disorder
Understanding functional connectivity alterations across manic, depressive, and remitted states of bipolar disorder (BD) remains an important challenge. In this cross-sectional study, we collected resting-state functional magnetic resonance imaging data from 117 BD patients, including manic BD patients (BipM: n = 38), depressive BD patients (BipD: n = 42), remitted BD patients (rBD: n = 37), and 35 healthy controls. [...]
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2026-11-12 11:22 |
[FZJ-2026-05920]
Journal Article/Dataset
Franco, J. ; Tewes, M. ; Zaun, G. ; et al
Wearable-Based Machine Learning Score for Fatigue: Initial Clinical Validation
Background: Fatigue is a prevalent and debilitating symptom among cancer patients (pts), yet routine assessment still relies on self-report instruments such as the FACIT-Fatigue (FACIT-F). To support continuous fatigue monitoring, we developed a wearable-derived Machine Learning Fatigue Score (ML-F; range 0–52, aligned with FACIT-F) in an observational study (72 pts, 22 healthy controls; 8 weeks), combining 35 physiological features from a fitness tracker (e.g., heart rate- and respiration-related measures) and anchoring the score to a single baseline FACIT-F assessment.Research Question: This study reports the initial clinical validation of the ML-F.Methods: Data were pooled from two prospective cohorts: (1) observational cohort and (2) an app-based feasibility study (38 pts; 4 weeks). [...]
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