Introduction to the Special Issue on Cognitive Neuroscience of Mindfulness.

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Published in final edited form as: Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Apr;10(4):337–341. doi: 10.1016/j.bpsc.2025.02.009 Search in PMC Search in PubMed View in NLM Catalog Add to search Introduction to the Special Issue on Cognitive Neuroscience of Mindfulness Todd S Braver Todd S Braver 1 Department of Psychological and Brain Sciences, Washington University in St. Louis 2 Department of Radiology, Washington University School of Medicine Find articles by Todd S Braver 1, 2 , Sara W Lazar Sara W Lazar 3 Department of Psychology, Harvard Medical School 4 Department of Psychiatry, Massachusetts General Hospital Find articles by Sara W Lazar 3, 4 Author information Copyright and License information 1 Department of Psychological and Brain Sciences, Washington University in St. Louis 2 Department of Radiology, Washington University School of Medicine 3 Department of Psychology, Harvard Medical School 4 Department of Psychiatry, Massachusetts General Hospital ✉ Correspondence concerning this article should be addressed to Todd S. Braver, Department of Psychological and Brain Science, Washington University in St. Louis, Campus Box 1125, One Brookings Drive, Saint Louis, MO 63130, United States. [email protected] . PMC Copyright notice PMCID: PMC13078108  NIHMSID: NIHMS2161005  PMID: 40189234 The publisher's version of this article is available at Biol Psychiatry Cogn Neurosci Neuroimaging “Mindfulness is … the miracle which can call back in a flash our dispersed mind and restore it to wholeness so that we can live each minute of life” The above quote was published exactly fifty years ago in the book Miracle of Mindfulness [ 1 ], written by the Vietnamese Buddhist monk and peace activist Thich Nhat Han. It is one of the first to bring a detailed description of mindfulness to a Western readership, in both practical terms and training exercises for daily living. Indeed, one dimension of mindfulness that has truly been miraculous is the way it has permeated so fully into Western culture and society over the last five decades, such that the term is now in common parlance within the lexicon. More strikingly, an estimated 14% of the US population now report practicing various forms of mindfulness and meditation, spawning an economic industry that is projected to be over $4 billion per year by 2027 (e.g., books, digital applications, instructors and therapists). This explosion of cultural interest in mindfulness has been paralleled by exponential growth of scientific research on mindfulness. Currently, nearly 3000 journal articles per year are now being published focused on this topic [ 2 ]. A large aspect of the scientific interest in mindfulness comes from its potential as a therapeutic intervention for a wide range of clinical and mental health disorders, ranging from anxiety and depression, to addiction, chronic pain, insomnia and others [ 3 ]. Yet in addition, there has also been a rapidly growing interest in the basic mechanisms by which mindfulness interventions and training might result in beneficial and salutary effects on cognitive and affective function, and general psychological well-being. A significant component of this work has adopted a cognitive neuroscience perspective, utilizing neuroimaging and other convergent methodologies, in order to understand salutary effects of mindfulness in terms of brain mechanisms of action [ 4 ]. In this regard, the Thich Nhat Hanh quote can be taken to refer not just to the miracle of mindfulness, but also to its mystery: How exactly do mindfulness practices enable the “calling back” of a dispersed mind, and its “return to wholeness”? Can the tools of cognitive neuroscience be deployed to better understand what that means in terms of cognitive-affective psychological and brain states? Why do these psychological and brain states help us to more fully “live each minute of life”? It is our hope that this Special Issue of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging shines a light on some of the most promising approaches and directions being taken in this area, in terms of the state-of-the-art methodologies, and current theoretical models being deployed to address these questions. The cognitive neuroscience of mindfulness: A brief history Before turning to the themes of this Special Issue, it is useful to briefly reflect on the history of cognitive neuroscience research on mindfulness meditation. The first published neuroimaging study dates back to 1955, when Das and Gastaut used EEG to compare meditation and resting-state activity in experienced Kriya yoga practitioners [ 5 ]. Over the next four decades, only a few dozen EEG studies were conducted, primarily with practitioners of yoga or Transcendental Meditation [ 6 ]. Mindfulness meditation remained largely absent from this early research, with the exception of a study by Dunn et al. [ 7 ]. A comprehensive review of the literature in 2006 concluded that no clear consensus had emerged regarding the neurophysiological effects of meditation, likely due to methodological inconsistencies, lack of standardized taxonomies, and an absence of robust theoretical frameworks [ 6 ]. The early 2000s marked a pivotal shift in the field. One of us (Lazar) conducted the first fMRI study of meditation, comparing Kundalini yoga meditation to a focused attention task, and demonstrating the utility of this approach [ 8 ]. This was followed by the first anatomical neuroimaging study, using MRI to identify differences in brain gray matter between experienced mindfulness practitioners and matched controls [ 9 ]. Around this same time, the Mind and Life Institute facilitated a series of dialogues between His Holiness the Dalai Lama and academic researchers in Dharamsala, India, with the first public event in the US occurring at a conference at MIT in 2003[ 10 ]. This was soon followed by a widely attended keynote address at the Society for Neuroscience conference in 2005. During this same period, Davidson et al. published the first EEG study specifically focused on mindfulness meditation [ 11 ], reporting a leftward shift in frontal asymmetry during resting-state EEG following an MBSR intervention, as well as changes in immune system function, compared to a wait-list control. Together, these early findings were striking, as they strongly suggested trait-like changes in brain structure and function related to meditation practice. The subsequent 10 years saw an influx of rigorous researchers into the field, catalyzing what can be described as the “first wave” of contemplative neuroscience. Studies focused on identifying the neural networks engaged by meditation, often within the context of mindfulness-based interventions (MBIs; [ 12 ]), though investigations also continued with highly experienced practitioners [ 13 – 16 ]. A particularly robust finding was the reduction in default mode network (DMN) activity during meditation, a pattern observed across multiple independent groups [ 17 – 19 ]. Other studies identified functional alterations in the amygdala in response to affective stimuli following meditation training [ 20 – 22 ]. Concurrently, researchers worked to establish theoretical models, refine standardized taxonomies of meditation states, and develop methodological standards for the design and reporting of clinical trials involving MBIs [ 23 – 26 ]. Over the last decade, cognitive neuroscience methodologies have undergone rapid advancements, reshaping the current landscape and future potential of mindfulness research. Within neuroimaging, a host of technological improvements have greatly enhanced the signal quality, resolution, and imaging modalities available, including high-field strength MRI scanners, diffusion imaging and other modalities (e.g., fNIRS), high-density and mobile EEG, concurrent physiological data acquisition. Concurrently, newer analytic approaches enable greater flexibility and sophistication in the types of information that can be extracted from the acquired data, including machine learning and multivariate pattern analysis (MVPA). Furthermore, technologies that enable direct modulation as well as monitoring of brain activity, such as real-time neurofeedback and noninvasive neurostimulation, have become widely available. As a consequence, the field has shifted from simple brain mapping towards embracing methods and approaches that focus on spatiotemporal dynamics within brain networks as well as in localized regions, and on the nature of representation and information contained within brain activity. Together, these shifts seem to mark the emergence of a “second wave” within contemplative neuroscience, which fully leverages the tools and technologies of 21 st -century cognitive neuroscience to address core questions within mindfulness research. This Special Issue came about as an outcome product of M 4 , the Mindfulness Mechanisms and Methods Meeting , which was a small-group conference and subsequent workshop that we co-organized at Washington University, St. Louis in October 2023. The goal of M 4 was to showcase this second wave of cognitive neuroscience research in mindfulness, with conference sessions organized to cover the current theoretical frameworks, technologies, and analytic methods being deployed within this domain. The articles that make up this Special Issue were contributed by the participants and speakers at M 4 . As we detail next, these articles provide short and topical conceptual reviews that parallel the sessions and key themes of the meeting. Additionally, it is important to note that there is a second Special Issue connected to M 4 , that is published in Biological Psychiatry: Global Open Science (BP:GOS), the sister journal of BP:CNNI. The BP:GOS Special Issue is complementary to this one, by focusing on new experimental research related to mindfulness, but including a wider range of topics, biologically-based methods, and research groups, beyond those that were present at M 4 . Consequently, we point to relevant articles in that journal as well as this one. Advances in analytic tools A prominent theme of M 4 and this Special Issue relates to advances in analytic tools, which can provide potentially more powerful means by which to reveal mindfulness effects in brain imaging data. Special Issue articles within this theme focus of emerging methods in EEG research, network neuroscience approaches, and how these have led to identification of new cognitive biomarkers, and the powerful role of multivariate predictive models. Although EEG is the oldest cognitive neuroscience technology that has been deployed to understand the neural basis of mindfulness, and mindfulness more broadly, it is also one of the most flexible and versatile. Lin et al [ 27 ] review recent methodological developments, illustrating how they have rejuvenated the use of EEG within mindfulness research. These include spectral decomposition approaches that enable identification of non-oscillatory EEG activity, information theoretic measures of neural complexity, identification of stable spatiotemporal patterns (microstates), and machine-learning based multivariate decoding. A primary goal of the article is to highlight the advantageous properties of EEG for studying the neurophysiology of mindfulness across a wide range of timescales and contexts, including comparative investigations that dissect the various ways in which the mindfulness construct has been operationalized (states, traits, skills, interventions). A parallel set of articles within the BP:GOS Special Issue provide excellent case studies of how some of these new EEG methods are being applied in current mindfulness research [ 28 – 31 ]. A major shift within neuroimaging methods over the past twenty years has been the emergence of a network-based approach to understanding brain organization and function [ 32 ]. In the subfield of network neuroscience, this focus is most explicitly realized, via the deployment of analytic tools and metrics that characterize brain networks, in terms of the nature of interactions between and within them [ 33 ]. Prakash et al [ 34 ] review the nascent literature that has begun to understand mindfulness effects in terms of changes to fundamental brain network properties. A tutorial overview is provided for unfamiliar readers, defining some of the key dimensions of network characterization, such small-worldness, modules, hub structures, and segregation / integration tradeoffs, along with the common metrics used to index them. The authors then describe some of the key initial findings emerging from the use of these metrics, including evidence that mindfulness modulates hub-like properties of anterior cingulate cortex, thalamus, and caudate, while also altering the connectivity properties of the DMN. Tripathi et al [ 35 ] further elaborate on the centrality of the DMN, and its interaction with other networks, for understanding mindfulness in terms of both state and trait-related changes. The authors review literature suggesting that DMN functional connectivity is modulated significantly by cognitive state fluctuations, including during mindfulness meditation, with interactions between the DMN and frontoparietal network (FPN) potentially serving as a predictor of mind-wandering versus focused attentional states. A parallel literature suggests that DMN connectivity may serve as a trait marker of a range of clinical and neurodevelopmental disorders, suggesting its potential utility as transdiagnostic indicator of brain network dysfunction. Methodological recommendations are provided regarding how to best estimate DMN connectivity for its usage as a biomarker of cognitive health. The BP:GOS Special Issue also includes articles which highlight the utility of novel network neuroscience methods, such as in revealing a hyperconnectivity signature of trait mindfulness [ 36 ] and increased integration as a trait marker of long-term meditators [ 37 ]. Another key development within cognitive neuroscience methodology has been a shifting emphasis towards analytic approaches that emphasize multivariate patterns within neural activity and behavior. In multivariate pattern analysis (MVPA), the key goal is to decode and quantify the degree of information contained within neural activity regarding task conditions or stimulus properties [ 38 , 39 ]. Well-established machine learning-based pattern classification algorithms (e.g., support vector machines), along with cross-validation approaches, are utilized to establish that the decoded patterns can be used in a truly predictive manner (i.e., applied towards held out data) [ 40 ]. Lewis-Peacock et al [ 41 ] focus on the utility of this type of multivariate predictive modeling as a powerful, but currently under-utilized tool for addressing core mindfulness questions, by highlighting two distinct research strategies. In the state induction approach, MVPA classifiers trained on experimentally manipulated mindfulness states are applied in new contexts to provide a dynamic readout of the practitioner’s mental state, capturing relevant fluctuations that may otherwise be hidden, such as transitions between focused attention and mind-wandering. In the neuromarker identification approach, a multivariate predictive model trained to classify a relevant psychological process of interest (e.g., pain, craving) is then applied to investigate the impact of mindfulness interventions on this process, using the predictive model as a neuromarker readout. The authors point out key issues to consider with this emerging mindfulness analytic tool, such as whether predictive models should be developed at the individual or population-level. Mindfulness neurotechnology Another prominent theme of M 4 that we also spotlight in this Special Issue are the current technologies available to modulate brain activity, and how these have and can be deployed to examine mindfulness-related questions. One of these is real-time neurofeedback, a closed-loop approach in which participants receive signal informing them of their own brain activity, most commonly through a visual display, and then try to modulate it towards some target value, receiving feedback about their degree of success [ 42 ]. Pioneering work in this domain demonstrated a link between feedback-modulated activation of the posterior cingulate cortex (PCC; [ 43 , 44 ] ), a key hub of the DMN, and subjective experience of various facets of mindfulness quality. Chen & Ziegler [ 45 ] provide a review of developments in closed-loop approaches and real-time neurofeedback, and how they have been leveraged both to understand the neural mechanisms of mindfulness, and as a means to develop new intervention approaches. Recent developments in both fMRI and EEG-based neurofeedback approaches are covered in the review, as well as critical methodological considerations when using this technology. These include the use of feedback based on network-based signals, rather than localized regions, the comparative effectiveness of different EEG spectral frequencies and portable EEG technologies and algorithms (e.g., Muse headband), and the importance of key control conditions, such as those utilizing sham neurofeedback. The promise of this technology, albeit as yet unrealized, is that it can be used as a personalized approach to optimize learning of mindfulness skills in novice meditator [ 46 ], or to enhance mindfulness intervention benefits in clinical populations [ 47 ]. Another promising technology, which has yet to reach its full potential as a tool to augment mindfulness training and interventions, is that of noninvasive neurostimulation. A number of neurostimulation tools have become widely available for use in cognitive neuroscience research, with the most popular being transcranial magnetic stimulation (TMS), and transcranial electrical stimulation (tES) [ 48 ]. Within the mindfulness research community, there has been increasing interest in whether neurostimulation has the potential to enhance training of mindfulness skills. Although some promising findings have been observed, overall the results have been somewhat mixed [ 49 ]. Lord et al [ 50 ] put forward a provocative perspective, namely, that current mixed findings might be related to both the mindfulness-based capacities being targeted as well as the neurostimulation technology being deployed. Literature is reviewed that suggests some of the strongest clinical benefits of mindfulness training are mediated via changes in equanimity, and interventions that directly target this capacity. Moreover, the authors highlight transcranial focused ultrasound (tFUS) as a highly flexible technology for neurostimulation, which may be particularly useful for targeting of equanimity. A key advantageous feature of tFUS is that stimulation targets can be precisely localized with high spatial precision, and further that deep brain structures, such as subcortical nuclei, which are more challenging or impossible to directly stimulate with other available methodologies, can also be targeted [ 51 ]. New conceptual and theoretical frameworks A third theme of M 4 highlighted in this Special Issue relates to theoretical frameworks and perspectives that get at the core phenomenology of mindfulness, and this can be understood in terms of underlying brain mechanisms. Hadjiilieva [ 52 ] provides a theoretical framework from which to characterize two distinct styles of mindfulness meditation, focused attention (FA) and open monitoring (OM), at the psychological and neurobiological levels, finding both shared and unique mechanisms employed by each. In particular, while FA is associated with deactivation of the DMN, OM is more associated with basal ganglia and cerebellum connectivity patterns. These findings are further contextualized within the author’s Dynamic Framework of Thought framework [ 53 ]. This framework, which distinguishes between deliberate and automatic constraints on thought, suggests that one potential mechanism underlying salutary effects of mindfulness is in decreasing automatically constrained forms of thought. The works of Bauer et al. and Lutz et al. explore the use of mindfulness as not just a tool for enhancing well-being, but also as a method for developing meta-cognitive awareness, which can be used to transform both research and clinical science. Bauer et al. [ 54 ] challenge the conventional view of the Observer Effect as a methodological nuisance, arguing instead that mindfulness can strengthen and refine self-observation, enhancing the precision of both psychological research and therapeutic interventions. They propose the creation of an “Observer Effect Index” to be used as an explanatory variable in cognitive neuroscience research. Lutz et al. [ 55 ] expand on this idea, reviewing recent advances in phenomenological assessment tools that capture different aspects of consciousness during meditation. This includes studies of advanced meditative states, core mental processes, and shifts in consciousness over various timeframes. A key focus is on the promise of Bayesian computational methods to formalize phenomenological interviews, and for building rich, comprehensive models of meditative states, including pain regulation and self-dissolution. The authors suggest that careful integration of phenomenology and predictive modeling, paired with measures of neural activity, can bring new insights in understanding mental health. Together these papers suggest that by embracing the observer as an active participant rather than a passive recorder, we open new frontiers in both psychological research and the treatment of mental disorders. A mindfulness consortium The final paper within this Special Issue introduces a global collaborative initiative aimed at advancing the neuroscientific study of meditation practices through large-scale meta- and megaanalyses of neuroimaging data. Ganesan et al [ 56 ] highlight the limitations of previous research, which has been constrained not only by small sample sizes and methodological inconsistencies, but also by heterogeneity in meditation techniques, study designs, and imaging protocols. By leveraging the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium framework, the working group seeks to standardize data processing and analysis to enhance statistical power, reproducibility, and generalizability. ENIGMA-Meditation aims to elucidate the neural mechanisms underlying meditation’s psychological and therapeutic effects, addressing its impacts on brain structure and function across diverse populations, including both healthy individuals and clinical groups. The initiative also explores meditation’s role in cognitive and emotional regulation, with the potential to inform clinical interventions and personalized therapeutic approaches Emerging trends in the cognitive neuroscience of mindfulness The aim of this Special Issue of BP:CNNI was to widely disseminate to the scientific community the current themes, most promising methodologies, and future research directions emerging within the cognitive neuroscience of mindfulness. This collection of articles provides an outstanding snapshot and summary of the discussions, enthusiasm, and collective energy conveyed by the speakers and participants in attendance at M 4 . Our inspiration in both organizing M 4 and bringing together this Special Issue was to hopefully communicate how this scientific energy might be best channeled towards to improve mental health outcomes and promote human flourishing, via advances in our understanding of mindfulness brain mechanisms. Although to us, this potential is quite clear, we believe it will be best achieved through translational approaches, which build from a strong foundation in basic cognitive neuroscience and neuroimaging research and methods towards high-priority clinical applications. We look forward with anticipation and excitement to see this potential realized. Acknowledgments This work was supported by NCCIH R13 AT011981, which provided funding for the Mindfulness Mechanisms & Methods Meeting (M 4 ) in October, 2023, out of which this Special Issue arose. Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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