Abstract
Editorial—Brain Connectivity
The human brain is not a collection of isolated regions, but a living, changing system of relationships. Its networks support perception, attention, memory, emotion, consciousness, and recovery. When these networks adapt, they can help us learn, meditate, compensate, and heal. When they reorganize maladaptively, they can contribute to phantom perception, traumatic distress, addiction, and impaired quality of life. The central question in our field is therefore no longer only where brain activity occurs, but how brain regions communicate, when these patterns change, and how we may safely guide them toward recovery.
This issue of Brain Connectivity brings together a timely set of contributions that map, model, and modulate the connected brain. Across resting-state functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electroencephalography (EEG), graph theory, brain–computer interfaces (BCI), machine learning, and noninvasive stimulation, these articles show how connectivity science is moving from cartography toward clinical translation. As the journal continues to grow, our mission remains constant: to publish rigorous, interdisciplinary work that advances systems neuroscience while keeping sight of the patients and communities who may ultimately benefit.
We open with a thoughtful commentary by Young-Eun Lee of Stanford University, who revisits functional connectome fingerprinting and argues that individuality in the brain is not fully captured by static, time-averaged connectivity. Rather, individual signatures may emerge most clearly in particular dynamic brain states. This shift from asking whether a person can be identified from a connectome to asking when individuality is most strongly expressed is highly relevant to precision neuroscience and personalized medicine.
In the special issue contribution on brain–computer interfaces, Chaochen Chen, Jie Li, and colleagues from Shanghai Yangzhi Rehabilitation Hospital and Tongji University address one of the most important barriers to clinical BCI translation: variability across users. Their multiview contrastive learning framework combines raw EEG and Hilbert-transformed EEG to improve cross-subject event-related potential (ERP) classification, while interpretability analyses show physiologically plausible patterns. This work reminds us that individual variability should not simply be averaged away; it can be modeled, learned from, and ultimately used to build more adaptable neurotechnology.
A complementary perspective on adaptive plasticity comes from Damisetti Geeta Prem Chandoo and colleagues at Dayalbagh Educational Institute, with collaborators at AIIMS New Delhi. Using MEG, they show that meditation is associated with stage- and frequency-specific changes in small-world network organization, particularly in attention and frontoparietal control systems. Their findings suggest that contemplative practice may tune the brain’s intrinsic network architecture toward more efficient information processing. At a time when mental health and cognitive resilience are global priorities, such work offers an important scientific bridge between ancient practices and modern network neuroscience.
The issue then turns to maladaptive reorganization through the study by Marie Detroz, Rajanikant Panda, and colleagues from the University of Liège. Their resting-state fMRI and graph-theoretical analysis of chronic tinnitus support the view that phantom sound is not merely an auditory problem, but a disorder of large-scale network organization. The observed reductions in clustering, local efficiency, small-worldness, and thalamic participation point to disrupted sensory integration and altered communication between local and distributed systems. This work advances a network-level understanding of tinnitus and highlights the thalamus as a key structure in the persistence of phantom perception.
In another clinically urgent domain, Olivier Roy, Shirley Fecteau, and colleagues from Université Laval, CERVO Brain Research Centre, examine resting-state functional connectivity in Canadian military personnel with adulthood-onset, war-related post-traumatic stress disorder (PTSD). They report that greater PTSD and anxiety symptoms are predominantly associated with lower connectivity, especially involving default mode and frontoparietal systems. Their findings also implicate visual networks in anxiety and time since trauma. Importantly, this work is framed not only as description, but as a step toward circuit-informed, personalized neuromodulatory interventions for people living with trauma.
Finally, Dylan H. Ballard, Gopalkumar Rakesh, and colleagues from the University of Kentucky, Yale, Duke, Emory, and the University of Chicago report a pilot study of accelerated intermittent theta burst stimulation targeting the left dorsolateral prefrontal cortex in people with opioid use disorder who smoke tobacco cigarettes. Although appropriately cautious because of the small sample, the study suggests that active stimulation may increase connectivity with the left supramarginal gyrus and reduce attentional bias toward cigarette cues. This is exactly the kind of translational, hypothesis-generating work our field needs: mechanistic, clinically grounded, and directed toward populations with urgent unmet needs.
Together, these papers make a powerful point: the same language of connectivity can describe the brain that is individualized, trained, disrupted, and therapeutically engaged. Dynamic fingerprinting shows us that individuality unfolds over time. Machine learning helps us generalize neural signals across people. Meditation research reveals adaptive network efficiency. Tinnitus and PTSD studies show how suffering can be embedded in altered communication among brain systems. Neuromodulation studies begin to test whether targeted stimulation can shift these systems toward healthier patterns.
The challenges ahead remain substantial. Sample sizes must grow, replication across sites and scanners must become routine, and the leap from group-level associations to individual prediction and treatment remains one of the defining tasks of our field. Yet this issue gives reason for optimism. The connected brain is increasingly becoming a brain we can measure more precisely, model more intelligently, and perhaps one day treat more personally.
I am grateful to the authors, reviewers, associate editors, editorial board members, and readers who continue to make Brain Connectivity a home for rigorous and clinically meaningful systems neuroscience. The future of our field will depend on collaboration among clinicians, engineers, computational scientists, psychologists, rehabilitation specialists, contemplative scientists, and patients themselves. To heal the brain, we must first understand its connections, and to understand its connections, we must remain open to the full complexity of the human person.