A Machine Learning Approach Towards the Differentiation Between Interoceptive and Exteroceptive Attention

Zuo, Zoey; Price, Cynthia; Farb, Norman A. S.

doi:10.1101/2022.06.10.495649

Cited by 2 publications

(3 citation statements)

References 73 publications

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“…Greater clarity is required on the functional specificity of such brain networks pertaining to meditation practices. Meditation may also involve whole-brain distributed or multivariate effects on brain function (91)(92)(93)(94) and structure (53,94), beyond their impact on localized regions, circuits, or networks. Robust decoding of such neural effects requires greater statistical power.…”

Section: Limitations In the Current State Of Meditation Neurosciencementioning

confidence: 99%

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ENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices

Ganesan,

Tsuchiyagaito,

Siegle

et al. 2024

Preprint

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Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualised meditation practices and designed various meditation interventions (MIs), that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has examined the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and non-clinical populations. However, the generalizability and replicability of current neuroscientific models of meditation are yet to be established, as they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural MRI). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA-Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights, accounting for multi-domain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms underlying therapeutic action of meditation practices on psychological and cognitive attributes, advancing the field of meditation and contemplative neuroscience.

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Section: Limitations In the Current State Of Meditation Neurosciencementioning

confidence: 99%

“…• Existing data-driven approaches to decode meditation states and outcomes developed using smaller datasets (91)(92)(93)(135)(136)(137) can be tested and improved using the large datasets and unified analytical framework available with ENIGMA-Meditation.…”

Section: Future Directionsmentioning

confidence: 99%

ENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices

Ganesan,

Tsuchiyagaito,

Siegle

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The answer to this question is not clear. In the work employing a state induction approach (30,31), the focus was on individualized assessment. That is, the target brain states were identified separately for each participant, and thus could be unique and idiosyncratic.…”

Section: Important Considerations For Future Mindfulness Researchmentioning

confidence: 99%

Decoding mindfulness with multivariate predictive models

Lewis-Peacock,

Wager,

Braver

2024

Preprint

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Identifying the brain mechanisms that underlie the salutary effects of mindfulness, meditation, and related practices is a critical goal of contemplative neuroscience. Here we suggest that the use of multivariate predictive models represents a promising and powerful methodology that could be better leveraged to pursue this goal. We describe the primary principles that underlie this approach, including multivariate decoding, predictive classification, and model-based analyses, all of which represent a strong departure from conventional brain mapping approaches. We highlight two such research strategies – state induction and neuromarker identification – and provide illustrative examples of how these approaches have been used to examine central questions in mindfulness, such as the distinction between internally directed focused attention and mind wandering, and the role of mindfulness interventions on somatic pain and drug-related cravings. We conclude by discussing important issues to be addressed with future research, including key tradeoffs between using a personalized versus population-based approach to predictive modeling.

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A Machine Learning Approach Towards the Differentiation Between Interoceptive and Exteroceptive Attention

Cited by 2 publications

References 73 publications

ENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices

ENIGMA-Meditation: Worldwide consortium for neuroscientific investigations of meditation practices

Decoding mindfulness with multivariate predictive models

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