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Singer B, Meling D, Hirsch-Hoffmann M, Michels L, Kometer M, Smigielski L, Dornbierer D, Seifritz E, Vollenweider FX, Scheidegger M. Psilocybin enhances insightfulness in meditation: a perspective on the global topology of brain imaging during meditation. Sci Rep 2024; 14:7211. [PMID: 38531905 PMCID: PMC10966054 DOI: 10.1038/s41598-024-55726-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/27/2024] [Indexed: 03/28/2024] Open
Abstract
In this study, for the first time, we explored a dataset of functional magnetic resonance images collected during focused attention and open monitoring meditation before and after a five-day psilocybin-assisted meditation retreat using a recently established approach, based on the Mapper algorithm from topological data analysis. After generating subject-specific maps for two groups (psilocybin vs. placebo, 18 subjects/group) of experienced meditators, organizational principles were uncovered using graph topological tools, including the optimal transport (OT) distance, a geometrically rich measure of similarity between brain activity patterns. This revealed characteristics of the topology (i.e. shape) in space (i.e. abstract space of voxels) and time dimension of whole-brain activity patterns during different styles of meditation and psilocybin-induced alterations. Most interestingly, we found that (psilocybin-induced) positive derealization, which fosters insightfulness specifically when accompanied by enhanced open-monitoring meditation, was linked to the OT distance between open-monitoring and resting state. Our findings suggest that enhanced meta-awareness through meditation practice in experienced meditators combined with potential psilocybin-induced positive alterations in perception mediate insightfulness. Together, these findings provide a novel perspective on meditation and psychedelics that may reveal potential novel brain markers for positive synergistic effects between mindfulness practices and psilocybin.
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Affiliation(s)
- Berit Singer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
| | - Daniel Meling
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
- Department of Psychosomatic Medicine and Psychotherapy, Medical Center - University of Freiburg, Freiburg, Germany
| | - Matthias Hirsch-Hoffmann
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, University Hospital Zurich, Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Michael Kometer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Lukasz Smigielski
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Dario Dornbierer
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Erich Seifritz
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
| | - Franz X Vollenweider
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland.
| | - Milan Scheidegger
- Department of Adult Psychiatry and Psychotherapy, Psychiatric University Clinic Zurich and University of Zurich, Zurich, Switzerland
- Department of Neuroradiology, University Hospital Zurich, Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
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Haşegan D, Geniesse C, Chowdhury S, Saggar M. Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.13.562304. [PMID: 37904918 PMCID: PMC10614807 DOI: 10.1101/2023.10.13.562304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2023]
Abstract
Capturing and tracking large-scale brain activity dynamics holds the potential to deepen our understanding of cognition. Previously, tools from Topological Data Analysis, especially Mapper, have been successfully used to mine brain activity dynamics at the highest spatiotemporal resolutions. Even though it is a relatively established tool within the field of Topological Data Analysis, Mapper results are highly impacted by parameter selection. Given that non-invasive human neuroimaging data (e.g., from fMRI) is typically fraught with artifacts and no gold standards exist regarding "true" state transitions, we argue for a thorough examination of Mapper parameter choices to better reveal their impact. Using synthetic data (with known transition structure) and real fMRI data, we explore a variety of parameter choices for each Mapper step, thereby providing guidance and heuristics for the field. We also release our parameter-exploration toolbox as a software package to make it easier for scientists to investigate and apply Mapper on any dataset.
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Affiliation(s)
- Daniel Haşegan
- Department of Psychiatry and Behavioral Sciences, Stanford University
| | - Caleb Geniesse
- Department of Psychiatry and Behavioral Sciences, Stanford University
| | - Samir Chowdhury
- Department of Psychiatry and Behavioral Sciences, Stanford University
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University
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Duman AN, Tatar AE. Topological data analysis for revealing dynamic brain reconfiguration in MEG data. PeerJ 2023; 11:e15721. [PMID: 37489123 PMCID: PMC10363343 DOI: 10.7717/peerj.15721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/16/2023] [Indexed: 07/26/2023] Open
Abstract
In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.
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Affiliation(s)
- Ali Nabi Duman
- Department of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Ahmet E. Tatar
- Center for Information Technology, University of Groningen, Groningen, Netherlands
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Zhang M, Chowdhury S, Saggar M. Temporal Mapper: Transition networks in simulated and real neural dynamics. Netw Neurosci 2023; 7:431-460. [PMID: 37397880 PMCID: PMC10312258 DOI: 10.1162/netn_a_00301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/07/2022] [Indexed: 07/26/2023] Open
Abstract
Characterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method-Temporal Mapper-built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects' behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics.
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Affiliation(s)
- Mengsen Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA
| | - Samir Chowdhury
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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Rasheed F, Jonsson D, Nilsson E, Masood TB, Hotz I. Subject-Specific Brain Activity Analysis in fMRI Data Using Merge Trees. 2022 TOPOLOGICAL DATA ANALYSIS AND VISUALIZATION (TOPOINVIS) 2022. [DOI: 10.1109/topoinvis57755.2022.00018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Blevins AS, Bassett DS, Scott EK, Vanwalleghem GC. From calcium imaging to graph topology. Netw Neurosci 2022; 6:1125-1147. [PMID: 38800465 PMCID: PMC11117109 DOI: 10.1162/netn_a_00262] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/13/2022] [Indexed: 05/29/2024] Open
Abstract
Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.
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Affiliation(s)
- Ann S. Blevins
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Ethan K. Scott
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, University of Melbourne, Parkville, Australia
| | - Gilles C. Vanwalleghem
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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