Abstract Non-suicidal self-injury (NSSI) represents a major health concern among adolescents with depression, yet no pharmacotherapies are specifically approved for NSSI. Selective serotonin reuptake inhibitors (SSRIs), commonly prescribed for adolescent depression, show inconsistent efficacy for NSSI, highlighting the need for neural signatures to predict NSSI outcomes. This study conducted a large-scale analysis of dynamic functional connectivity in 452 adolescents (332 drug-naïve patients with first-episode depression, including 178 with and 154 without NSSI, and 120 healthy controls) from an open-label SSRI trial (ChiCTR2000039503), examining how brain dynamics relate to the frequency and treatment outcomes of NSSI. Clinical and neuroimaging data were collected at baseline, with follow-up clinical assessments at 8 weeks and 14 months during SSRI treatment. Dynamic brain states and variability in graph-theoretical metrics were derived from imaging data, and the reproducibility of temporal dynamic patterns was validated in an independent dataset ( N = 112). Adolescents with depression and NSSI exhibited prolonged engagement in a biologically reproducible, relatively low-modularized, and moderately connected brain state, as well as elevated variability in small-world network metrics. This state was characterized by altered connectivity in the default mode and frontoparietal networks. Notably, baseline fractional windows of this state predicted both short-term reduction in NSSI frequency at 8 weeks and NSSI cessation at 14 months following SSRI treatment. These findings were consistently replicated across multiple validation analyses. Our results identify dynamic brain network patterns linked to NSSI outcomes during SSRI treatment, suggesting that characterizing such dynamic properties may help identify individuals at risk of poor NSSI outcomes and inform early intervention in adolescent depression.
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
A comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms is provided, namely: business challenges, organizational challenges, technical challenges, and people challenges.
Yaser Ghanam, F. Maurer, P. Abrahamsson· Information and Software Tec...· 41 citations· ⚡3
It is shown that high article processing charges are not sufficiently justified by the publishers, which often lack transparency and may prevent authors from adopting OA.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· Scientometrics· 21 citations· ⚡1
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.
Yang Yue, Shu Li, Yihua Cheng et al.· bioRxiv· 15 citations
Abstract Predicting properties from coordinate–category data—sets of vectors paired with categorical information—is fundamental to computational science, manifesting in materials science as predicting formation energies or elastic moduli from crystal structures. While large language models (LLMs) have increasingly been...
Nawaf Alampara, Santiago Miret, Kevin Maik Jablonka· Nature Computational Science· 13 citations
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al.· Journal of Chemical Informat...· 13 citations· ⚡1
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