Abstract Childhood maltreatment (CM) is a risk factor for adult post-traumatic symptoms and white matter (WM) alterations. It remains unknown how WM microstructure is affected alongside two common military conditions, post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI). Data from 299 participants (53% male, 35.69 ± 12.23 years of age) of the Injury and Traumatic Stress (INTRuST) clinical consortium was analyzed. The study included 56 individuals with PTSD (41 with comorbid mTBI), 107 with mTBI, and 136 controls (no PTSD and/or mTBI). Diffusion parameters (mean, axial, and radial diffusivity, MD, AD, RD; fractional anisotropy, FA) in major WM tracts were extracted from diffusion MRI. CM was assessed using the Childhood Trauma Questionnaire (CTQ). Multiple regression models examined associations between CM severity and WM diffusion. Moderation analyses assessed the effect of PTSD and/or mTBI on these associations. Mediation analyses examined how WM diffusion affects the associations between CM and both PTSD and post-concussive symptom severity. Greater CM severity was associated with lower MD and AD, independent of PTSD and/or mTBI. WM diffusion, particularly in the corpus callosum and the superior longitudinal fasciculus, mediated the associations between CM severity and both PTSD (MD b ab = 0.022, AD b ab = 0.019-0.034) and post-concussive symptoms (MD b ab = 0.028-0.040, AD b ab = 0.032-0.047). Findings suggest that WM alterations are more strongly associated with CM itself than with either PTSD and/or mTBI. Mediating effects in regions critical for sensory integration highlight the role of WM microstructure as a neurobiological pathway through which CM might predispose to post-traumatic symptoms.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new an...
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Related blog posts
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.