The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.
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http://dx.doi.org/10.1016/j.neuroimage.2024.120558 | DOI Listing |
PLOS Glob Public Health
January 2025
Research Care Training Program, Centre for Microbiology Research, Kenya Medical Research Institute, Kisumu, Kenya.
Structural, psychological, and clinical barriers to HIV care engagement among adolescents and young adults living with HIV (AYAH) persist globally despite gains in HIV epidemic control. Phone-based peer navigation may provide critical peer support, increase delivery flexibility, and require fewer resources. Prior studies show that phone-based navigation and automated text messaging interventions improve HIV care engagement, adherence, and retention among AYAH.
View Article and Find Full Text PDFPLoS One
January 2025
ESQlabs Gmbh, Saterland, Germany.
Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States of America.
Complex systems, such as in brains, markets, and societies, exhibit internal dynamics influenced by external factors. Disentangling delayed external effects from internal dynamics within these systems is often difficult. We propose using a Vector Autoregressive model with eXogenous input (VARX) to capture delayed interactions between internal and external variables.
View Article and Find Full Text PDFPLOS Digit Health
January 2025
School of Nursing, McMaster University, Hamilton, Ontario, Canada.
The multicomponent Remission Evaluation of Medical Interventions in T2D (REMIT) program has shown reduction of hazard of diabetes relapse by 34-43%, but could benefit from improved ability to scale, spread, and sustain it. This study explored, at the conceptualization phase, patient and health coach perspectives on the acceptability, adoption, feasibility, and appropriateness of a digital REMIT adaptation (diabetes technology enabled coaching (DTEC)). Twelve semi-structured interviews were conducted with patients (n = 6) and health coaches (n = 6) to explore their experiences with the REMIT study, opportunities for virtualisation, and a cognitive walkthrough of solution concepts.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138.
Despite the broad catalytic relevance of metal-support interfaces, controlling their chemical nature, the interfacial contact perimeter (exposed to reactants), and consequently, their contributions to overall catalytic reactivity, remains challenging, as the nanoparticle and support characteristics are interdependent when catalysts are prepared by impregnation. Here, we decoupled both characteristics by using a raspberry-colloid-templating strategy that yields partially embedded PdAu nanoparticles within well-defined SiO or TiO supports, thereby increasing the metal-support interfacial contact compared to nonembedded catalysts that we prepared by attaching the same nanoparticles onto support surfaces. Between nonembedded PdAu/SiO and PdAu/TiO, we identified a support effect resulting in a 1.
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