Personalized brain network models for assessing structure–function relationships

Bansal, Kanika; Nakuci, Johan; Muldoon, Sarah F.

doi:10.1016/j.conb.2018.04.014

Cited by 80 publications

(67 citation statements)

References 51 publications

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“…One obvious limitation of the current research design is that the data were collected from a single participant, which makes it impossible to examine potential individual differences in our findings. However, although research in cognitive neuroscience typically collects data from multiple participants, for the majority of studies, their main focus is on the aggregated pattern of the brain activation/connectivity (but see person-centered research, e.g., (25)), and individual differences have been typically treated as random noise (sampling error). Therefore, in our view, this limitation is superseded by the strength of the current design: sensitivity to the nuanced intra-individual changes in brain signals and functional connectivity.…”

Section: Discussionmentioning

confidence: 99%

“…This inter-individual aggregation approach is useful to examine the effects of meditation averaged across participants. However, given the large individual differences in the whole-brain functional connectivity pattern (25,26), there is danger that the approach potentially masks important intra-individual changes in the composition of the brain networks (e.g., some participant-specific network structures may be canceled out by inter-individual aggregation). Therefore, adopting a design that allows us to focus on the intra-individual change may provide novel insights into how mindfulness meditation alters the structure of the brain networks.…”

Section: Introductionmentioning

confidence: 99%

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Focused attention meditation changes the boundary and configuration of functional networks in the brain

Kajimura

Masuda

Lau

et al. 2019

Preprint

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1Research has shown that mindfulness meditation not only improves our cognitive and 2 motivational functioning (e.g., attention, mental health), it influences the way how our brain 3 networks [e.g., default mode network (DMN), front-parietal network (FPN), and sensory-motor 4 network (SMN)] function and operate. However, surprisingly little attention has been paid to the 5 possibility that mindfulness meditation alters the structure (composition) of these functional brain 6 networks. Here, using a single-case experimental design with longitudinal intensive data, we 7 examined the effect of mindfulness mediation on intra-individual changes in the composition of 8 whole-brain networks. The results showed that mindfulness meditation (1) changed the community 9 size (with a number of regions in the FPN being merged into the DMN after mindfulness 10 meditation), (2) changed the brain regions composing the SMN community without changing its 11 size, and (3) led to instability in the community allegiance of the regions in the FPN. These results 12 suggest that, in addition to altering specific functional connectivity, mindfulness meditation leads to 13 reconfiguration of whole-brain network structure. The reconfiguration of community structure in the 14 brain provides fruitful information about the neural mechanisms of mindfulness meditation. 15 16 3 Author Summary 1Training to focus on present-moment experiences, i.e., mindfulness meditation, is known 2 to produce significant beneficial outcomes, such as improved attention and mental health. It is 3 important to understand how these beneficial effects are produced at the neural level; however, 4 previous research investigating this question almost exclusively employed a pre-post design (i.e. 5 assessing brain functioning only once or twice) or comparison between beginners and experts. The 6 current study takes a fundamentally different approach ----we repeatedly assess the brain function of 7 a single person over many days (intensive longitudinal design) and show that mindfulness mediation 8 significantly changes the size, composition, and variability of the brain networks that are related to 9 sensory processing, executive control, or spontaneous thoughts and self-referential processing. These 10 results highlight the importance of investigating intraindividual changes for understanding the neural 11 mechanisms of mindfulness meditation and suggest that such changes may be used as a biomarker to 12 reflect the efficacy and progress of mindfulness meditation. 13 14 15 1

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Focused attention meditation changes the boundary and configuration of functional networks in the brain

Kajimura

Masuda

Lau

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…If the critical features of pathological networks can be effectively captured in computational models, they can be used for diagnosis and delivery of personalized therapeutic weak electric fields (Figure 1). Multiple studies in theoretical and computational neuroscience have developed whole-brain network models [38][39][40][41] to explore the relationship between brain function and its underlying connectivity. This increased interest in finding the origin of the structure-function relationship has led to a newly developing field known as network neuroscience (Bassett and Sporns, 2017) [42] that relies on graph theory to study the brain across its multiple scales and complexities.…”

Section: Tcs and Brain Network: From Biophysics To Physiologymentioning

confidence: 99%

Targeting brain networks with multichannel transcranial current stimulation (tCS)

Ruffini

Wendling

Sanchez-Todo³

et al. 2018

Current Opinion in Biomedical Engineering

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The brain is a complex, plastic, electrical network whose dysfunctions result in neurological disorders. Multichannel transcranial electrical stimulation (tCS) is a non-invasive neuromodulatory technique with the potential for network-oriented therapy. Challenges to realizing this vision include the proper identification of involved networks in a patient-specific context, a deeper understanding of the effects of stimulation on interconnected neuronal populations-both immediate and plastic-and, based on these, developing strategies to personalize brain stimulation interventions. For this reason, personalized hybrid biophysical and physiological models of brain networks are poised to play a key role in the evolution of networkoriented transcranial stimulation. We review some of the recent work in this emerging area of research and provide an outlook for future modeling and experimental work, as well as for developing its clinical applications in fields such as epilepsy. Research Projects Activity (IARPA), via 2014-13121700007. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. ES is supported by the Beth Israel Deaconess Medical Center (BIDMC) via the Chief Academic Officer (CAO) Award 2017, and the Defense Advanced Research Projects Agency (DARPA) via HR001117S0030. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of Harvard University, and its affiliated academic health care centers. DECLARATION OF INTEREST Giulio Ruffini is a shareholder and works for Neuroelectrics, a company designing medical devices for brain stimulation. Roser Sanchez-Todo is a researcher at Neuroelectrics. REFERENCES

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“…This inter-individual aggregation approach is useful to examine the effects of meditation averaged across participants. However, given the large individual differences in the whole-brain functional connectivity patterns 24 , 25 , there is danger that the approach potentially masks important intra-individual changes in the composition of the brain networks (e.g., some participant-specific network architectures may be canceled out by inter-individual aggregation). Therefore, adopting a design that allows us to focus on the intra-individual change may provide novel insights into how meditation alters the architecture of the brain networks.…”

Section: Introductionmentioning

confidence: 99%

Focused attention meditation changes the boundary and configuration of functional networks in the brain

Kajimura

Masuda

Lau

et al. 2020

Sci Rep

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Research has shown that focused attention meditation not only improves our cognitive and motivational functioning (e.g., attention, mental health), it influences the way our brain networks [e.g., default mode network (DMN), fronto-parietal network (FPN), and sensory-motor network (SMN)] function and operate. However, surprisingly little attention has been paid to the possibility that meditation alters the architecture (composition) of these functional brain networks. Here, using a single-case experimental design with intensive longitudinal data, we examined the effect of mediation practice on intra-individual changes in the composition of whole-brain networks. The results showed that meditation (1) changed the community size (with a number of regions in the FPN being merged into the DMN after meditation) and (2) led to instability in the community allegiance of the regions in the FPN. These results suggest that, in addition to altering specific functional connectivity, meditation leads to reconfiguration of whole-brain network architecture. The reconfiguration of community architecture in the brain provides fruitful information about the neural mechanisms of meditation.

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Personalized brain network models for assessing structure–function relationships

Cited by 80 publications

References 51 publications

Focused attention meditation changes the boundary and configuration of functional networks in the brain

Focused attention meditation changes the boundary and configuration of functional networks in the brain

Targeting brain networks with multichannel transcranial current stimulation (tCS)

Focused attention meditation changes the boundary and configuration of functional networks in the brain

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