Guest Editors
Majid Saberi
University of Michigan
Dr. Majid Saberi is a research fellow at the University of Michigan with a PhD in cognitive science, graduate training in physics, and expertise in computational neuroscience and neuroimaging. His research focuses on how chronic pain disrupts large-scale brain dynamics, neural complexity, and adaptive network organization. He integrates neuroimaging, nonlinear dynamics and complexity analysis, graph theory, and network energy modeling to identify dynamical markers of maladaptive brain-state organization in chronic pain disorders. His work also examines treatment-related restoration of more adaptive neural dynamics and emphasizes transparent, reproducible, and accessible analytical workflows for studying brain network dynamics in chronic pain. As guest editor, he will help shape a collection that highlights rigorous and accessible protocols for studying brain network dynamics in chronic pain and support the development of network-informed interventional approaches.
Shima Hassanpour
Laval University
Dr. Shima Hassanpour is a neuroscientist whose research focuses on the neural mechanisms of chronic pain. She completed her PhD in neuroscience at Queen's University, where she investigated pain modulation using brainstem and spinal cord functional MRI, together with eye-tracking and physiological measures. Her research combines neuroimaging, EEG, computational neuroscience, and advanced data analysis to study how chronic pain affects brain function and pain regulation. She is particularly interested in identifying neural biomarkers of chronic pain and understanding individual differences in pain perception.As a guest editor, she aims to support a collection that brings together high-quality research on the neural mechanisms of chronic pain, with an emphasis on neuroimaging, computational approaches, multimodal biomarkers, and emerging analytical methods. She is committed to promoting rigorous, reproducible, and collaborative research that advances our understanding of pain and supports the development of more effective, evidence-based interventions.
Collection Overview
Chronic pain is increasingly understood from a network perspective, in which the dynamics of distributed brain systems contribute to pain perception, persistence, and treatment response. Rather than focusing only on isolated brain regions or static neural markers, this perspective emphasizes how sensory, cognitive-affective, autonomic, and descending modulatory systems coordinate, reorganize, and interact over time. These dynamics may be examined within short recording periods, during rest or task performance, across disease progression, or before and after therapeutic interventions.
This Topical Collection will highlight experimental, computational, and analytical protocols for studying the dynamics of brain networks in chronic pain. Relevant approaches may include resting-state and task-based neuroimaging, spectral and time-frequency analyses, dynamic functional connectivity, graph-theoretical network analysis, effective connectivity, nonlinear dynamics and complexity analyses, multidimensional data integration, and transparent, reproducible analysis pipelines. By translating these approaches into JoVE’s video-based format, the collection will support standardization of data acquisition, preprocessing, network analysis, modeling, visualization, and interpretation across chronic pain conditions.
Designed for pain neuroscientists and trainees, this collection will emphasize adaptable and reproducible methods that improve methodological consistency, facilitate cross-study comparison, and support mechanistic discovery. Ultimately, it aims to encourage the development of network-informed biomarkers and therapeutic strategies for chronic pain.