1
|
Rahrig H, Ma L, Brown KW, Martelli AM, West SJ, Lasko EN, Chester DS. Inside the mindful moment: The effects of brief mindfulness practice on large-scale network organization and intimate partner aggression. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:1581-1597. [PMID: 37880570 PMCID: PMC10842035 DOI: 10.3758/s13415-023-01136-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/06/2023] [Indexed: 10/27/2023]
Abstract
Mindfulness can produce neuroplastic changes that support adaptive cognitive and emotional functioning. Recently interest in single-exercise mindfulness instruction has grown considerably because of the advent of mobile health technology. Accordingly, the current study sought to extend neural models of mindfulness by investigating transient states of mindfulness during single-dose exposure to focused attention meditation. Specifically, we examined the ability of a brief mindfulness induction to attenuate intimate partner aggression via adaptive changes to intrinsic functional brain networks. We employed a dual-regression approach to examine a large-scale functional network organization in 50 intimate partner dyads (total n = 100) while they received either mindfulness (n = 50) or relaxation (n = 50) instruction. Mindfulness instruction reduced coherence within the Default Mode Network and increased functional connectivity within the Frontoparietal Control and Salience Networks. Additionally, mindfulness decoupled primary visual and attention-linked networks. Yet, this induction was unable to elicit changes in subsequent intimate partner aggression, and such aggression was broadly unassociated with any of our network indices. These findings suggest that minimal doses of focused attention-based mindfulness can promote transient changes in large-scale brain networks that have uncertain implications for aggressive behavior.
Collapse
Affiliation(s)
- Hadley Rahrig
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA.
| | - Liangsuo Ma
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - Kirk Warren Brown
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
- Carnegie Mellon University, Pittsburgh, PA, USA
| | | | | | - Emily N Lasko
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| | - David S Chester
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA
| |
Collapse
|
2
|
Wang Y, Guo Y. LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY. Ann Appl Stat 2023; 17:1307-1332. [PMID: 39040949 PMCID: PMC11262594 DOI: 10.1214/22-aoas1670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major challenges in analyzing connectivity matrices, including the high dimensionality of brain networks, unknown latent sources underlying the observed connectivity, and the large number of brain connections leading to spurious findings. In this paper we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures. Compared with the existing method that vectorizes connectivity matrices ignoring brain network topology, LOCUS achieves more efficient and accurate source separation for connectivity matrices using low-rank structure. We propose a novel angle-based uniform sparsity regularization that demonstrates better performance than the existing sparsity controls for low-rank tensor methods. We propose a highly efficient iterative node-rotation algorithm that exploits the block multiconvexity of the objective function to solve the nonconvex optimization problem for learning LOCUS. We illustrate the advantage of LOCUS through extensive simulation studies. Application of LOCUS to Philadelphia Neurodevelopmental Cohort neuroimaging study reveals biologically insightful connectivity traits which are not found using the existing method.
Collapse
Affiliation(s)
- Yikai Wang
- Department of Biostatistics and Bioinformatics, Emory University
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University
| |
Collapse
|
3
|
Spatiotemporal EEG Dynamics of Prospective Memory in Ageing and Mild Cognitive Impairment. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10075-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Abstract
Prospective memory (PM, the memory of future intentions) is one of the first complaints of those that develop dementia-related disease. Little is known about the neurophysiology of PM in ageing and those with mild cognitive impairment (MCI). By using a novel artificial neural network to investigate the spatial and temporal features of PM related brain activity, new insights can be uncovered. Young adults (n = 30), healthy older adults (n = 39) and older adults with MCI (n = 27) completed a working memory and two PM (perceptual, conceptual) tasks. Time-locked electroencephalographic potentials (ERPs) from 128-electrodes were analysed using a brain-inspired spiking neural network (SNN) architecture. Local and global connectivity from the SNNs was then evaluated. SNNs outperformed other machine learning methods in classification of brain activity between younger, older and older adults with MCI. SNNs trained using PM related brain activity had better classification accuracy than working memory related brain activity. In general, younger adults exhibited greater local cluster connectivity compared to both older adult groups. Older adults with MCI demonstrated decreased global connectivity in response to working memory and perceptual PM tasks but increased connectivity in the conceptual PM models relative to younger and healthy older adults. SNNs can provide a useful method for differentiating between those with and without MCI. Using brain activity related to PM in combination with SNNs may provide a sensitive biomarker for detecting cognitive decline. Cognitively demanding tasks may increase the amount connectivity in older adults with MCI as a means of compensation.
Collapse
|
4
|
Cooper AC, Ventura B, Northoff G. Beyond the veil of duality-topographic reorganization model of meditation. Neurosci Conscious 2022; 2022:niac013. [PMID: 36237370 PMCID: PMC9552929 DOI: 10.1093/nc/niac013] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 08/08/2022] [Accepted: 09/01/2022] [Indexed: 11/14/2022] Open
Abstract
Meditation can exert a profound impact on our mental life, with proficient practitioners often reporting an experience free of boundaries between a separate self and the environment, suggesting an explicit experience of "nondual awareness." What are the neural correlates of such experiences and how do they relate to the idea of nondual awareness itself? In order to unravel the effects that meditation has on the brain's spatial topography, we review functional magnetic resonance imaging brain findings from studies specific to an array of meditation types and meditator experience levels. We also review findings from studies that directly probe the interaction between meditation and the experience of the self. The main results are (i) decreased posterior default mode network (DMN) activity, (ii) increased central executive network (CEN) activity, (iii) decreased connectivity within posterior DMN as well as between posterior and anterior DMN, (iv) increased connectivity within the anterior DMN and CEN, and (v) significantly impacted connectivity between the DMN and CEN (likely a nonlinear phenomenon). Together, these suggest a profound organizational shift of the brain's spatial topography in advanced meditators-we therefore propose a topographic reorganization model of meditation (TRoM). One core component of the TRoM is that the topographic reorganization of DMN and CEN is related to a decrease in the mental-self-processing along with a synchronization with the more nondual layers of self-processing, notably interoceptive and exteroceptive-self-processing. This reorganization of the functionality of both brain and self-processing can result in the explicit experience of nondual awareness. In conclusion, this review provides insight into the profound neural effects of advanced meditation and proposes a result-driven unifying model (TRoM) aimed at identifying the inextricably tied objective (neural) and subjective (experiential) effects of meditation.
Collapse
Affiliation(s)
- Austin Clinton Cooper
- Integrated Program of Neuroscience, Room 302, Irving Ludmer Building, 1033 Pine Avenue W., McGill University, Montreal, QC H3A 1A1, Canada
| | - Bianca Ventura
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics Research Unit, Institute of Mental Health Research, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada
- Mental Health Center, Zhejiang University School of Medicine, 866 Yuhangtang Road, Hangzhou 310058, China
| |
Collapse
|
5
|
Ganesan S, Beyer E, Moffat B, Van Dam NT, Lorenzetti V, Zalesky A. Focused attention meditation in healthy adults: A systematic review and meta-analysis of cross-sectional functional MRI studies. Neurosci Biobehav Rev 2022; 141:104846. [PMID: 36067965 DOI: 10.1016/j.neubiorev.2022.104846] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/17/2022] [Accepted: 08/26/2022] [Indexed: 10/14/2022]
Abstract
Meditation trains the mind to focus attention towards an object or experience. Among different meditation techniques, focused attention meditation is considered foundational for more advanced practices. Despite renewed interest in its functional neural correlates, there is no unified neurocognitive model of focused attention meditation developed via quantitative synthesis of contemporary literature. Hence, we performed a quantitative systematic review and meta-analysis of all functional MRI studies examining focussed attention meditation. Following PRISMA guidelines, 28 studies were included in this review, of which 10 studies (200 participants) were amenable to activation likelihood estimation meta-analysis. We found that regions comprising three key functional brain networks i.e., Default-mode, Salience, and Executive Control, were consistently implicated in focused attention meditation. Furthermore, meditation expertise, mindfulness levels and attentional skills were found to significantly influence the magnitude, but not regional extent, of activation and functional connectivity in these networks. Aggregating all evidence, we present a unified neurocognitive brain-network model of focused attention meditation.
Collapse
Affiliation(s)
- Saampras Ganesan
- Melbourne Neuropsychiatry Centre, Carlton, Victoria 3053, Australia; Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia.
| | - Emillie Beyer
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Fitzroy, Victoria 3065, Australia.
| | - Bradford Moffat
- Melbourne Brain Centre Imaging Unit, Department of Radiology, The University of Melbourne, Parkville, Victoria 3052, Australia.
| | - Nicholas T Van Dam
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria 3010, Australia.
| | - Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioral and Health Sciences, Faculty of Health, Australian Catholic University, Fitzroy, Victoria 3065, Australia.
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Carlton, Victoria 3053, Australia; Department of Biomedical Engineering, The University of Melbourne, Carlton, Victoria 3053, Australia.
| |
Collapse
|
6
|
Rahrig H, Vago DR, Passarelli MA, Auten A, Lynn NA, Brown KW. Meta-analytic evidence that mindfulness training alters resting state default mode network connectivity. Sci Rep 2022; 12:12260. [PMID: 35851275 PMCID: PMC9293892 DOI: 10.1038/s41598-022-15195-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 06/20/2022] [Indexed: 11/09/2022] Open
Abstract
This meta-analysis sought to expand upon neurobiological models of mindfulness through investigation of inherent brain network connectivity outcomes, indexed via resting state functional connectivity (rsFC). We conducted a systematic review and meta-analysis of rsFC as an outcome of mindfulness training (MT) relative to control, with the hypothesis that MT would increase cross-network connectivity between nodes of the Default Mode Network (DMN), Salience Network (SN), and Frontoparietal Control Network (FPCN) as a mechanism of internally-oriented attentional control. Texts were identified from the databases: MEDLINE/PubMed, ERIC, PSYCINFO, ProQuest, Scopus, and Web of Sciences; and were screened for inclusion based on experimental/quasi-experimental trial design and use of mindfulness-based training interventions. RsFC effects were extracted from twelve studies (mindfulness n = 226; control n = 204). Voxel-based meta-analysis revealed significantly greater rsFC (MT > control) between the left middle cingulate (Hedge's g = .234, p = 0.0288, I2 = 15.87), located within the SN, and the posterior cingulate cortex, a focal hub of the DMN. Egger's test for publication bias was nonsignificant, bias = 2.17, p = 0.162. In support of our hypothesis, results suggest that MT targets internetwork (SN-DMN) connectivity implicated in the flexible control of internally-oriented attention.
Collapse
Affiliation(s)
- Hadley Rahrig
- Department of Psychology, Virginia Commonwealth University, 806 W. Franklin Street, Richmond, VA, 23284, USA.
| | - David R Vago
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt University, Nashville, USA, TN
| | - Matthew A Passarelli
- Department of Psychology, Virginia Commonwealth University, 806 W. Franklin Street, Richmond, VA, 23284, USA
| | - Allison Auten
- Department of Psychology, Virginia Commonwealth University, 806 W. Franklin Street, Richmond, VA, 23284, USA
| | - Nicholas A Lynn
- Department of Psychology, Virginia Commonwealth University, 806 W. Franklin Street, Richmond, VA, 23284, USA
| | - Kirk Warren Brown
- Department of Psychology, Virginia Commonwealth University, 806 W. Franklin Street, Richmond, VA, 23284, USA.
| |
Collapse
|
7
|
Meditation-induced effects on whole-brain structural and effective connectivity. Brain Struct Funct 2022; 227:2087-2102. [PMID: 35524072 PMCID: PMC9232427 DOI: 10.1007/s00429-022-02496-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 04/04/2022] [Indexed: 12/26/2022]
Abstract
In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in functional dynamics and structural connectivity (SC). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain’s structure and function. First, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals’ spatio-temporal structure, akin to functional connectivity (FC) with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. Then, we performed a network-based analysis of anatomical connectivity. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. We showed that the most informative EC links that discriminated between meditators and controls involved several large-scale networks mainly within the left hemisphere. Moreover, we found that differences in the functional domain were reflected to a smaller extent in changes at the anatomical level as well. The network-based analysis of anatomical pathways revealed strengthened connectivity for meditators compared to controls between four areas in the left hemisphere belonging to the somatomotor, dorsal attention, subcortical and visual networks. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.
Collapse
|
8
|
Savanth AS, Vijaya PA, Nair AK, Kutty BM. Classification of Rajayoga Meditators Based on the Duration of Practice Using Graph Theoretical Measures of Functional Connectivity from Task-Based Functional Magnetic Resonance Imaging. Int J Yoga 2022; 15:96-105. [PMID: 36329777 PMCID: PMC9623885 DOI: 10.4103/ijoy.ijoy_17_22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 07/05/2022] [Accepted: 07/11/2022] [Indexed: 01/24/2023] Open
Abstract
CONTEXT Functional magnetic resonance imaging (fMRI) studies on mental training techniques such as meditation have reported benefits like increased attention and concentration, better emotional regulation, as well as reduced stress and anxiety. Although several studies have examined functional activation and connectivity in long-term as well as short-term meditators from different meditation traditions, it is unclear if long-term meditation practice brings about distinct changes in network properties of brain functional connectivity that persist during task performance. Indeed, task-based functional connectivity studies of meditators are rare. AIMS This study aimed to differentiate between long-term and short-term Rajayoga meditators based on functional connectivity between regions of interest in the brain. Task-based fMRI was captured as the meditators performed an engaging task. The graph theoretical-based functional connectivity measures of task-based fMRI were calculated using CONN toolbox and were used as features to classify the two groups using Machine Learning models. SUBJECTS AND METHODS In this study, we recruited two age and sex-matched groups of Rajayoga meditators from the Brahma Kumaris tradition that differed in the duration of their meditation experience: Long-term practitioners (n = 12, mean 13,596 h) and short-term practitioners (n = 10, mean 1095 h). fMRI data were acquired as they performed an engaging task and functional connectivity metrics were calculated from this data. These metrics were used as features in training machine learning algorithms. Specifically, we used adjacency matrices generated from graph measures, global efficiency, and local efficiency, as features. We computed functional connectivity with 132 ROIs as well as 32 network ROIs. STATISTICAL ANALYSIS USED Five machine learning models, such as logistic regression, SVM, decision tree, random forest, and gradient boosted tree, were trained to classify the two groups. Accuracy, precision, sensitivity, selectivity, area under the curve receiver operating characteristics curve were used as performance measures. RESULTS The graph measures were effective features, and tree-based algorithms such as decision tree, random forest, and gradient boosted tree yielded the best performance (test accuracy >84% with 132 ROIs) in classifying the two groups of meditators. CONCLUSIONS Our results support the hypothesis that long-term meditative practices alter brain functional connectivity networks even in nonmeditative contexts. Further, the use of adjacency matrices from graph theoretical measures of high-dimensional fMRI data yields a promising feature set for machine learning classifiers.
Collapse
Affiliation(s)
- Ashwini S. Savanth
- Department of Electronics and Communication Engineering, BNM Institute of Technology, Bangalore and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India,Address for correspondence: Prof. Ashwini S. Savanth, Department of Electronics and Communication Engineering, BNM Institute of Technology, Banashankari 2nd Stage, Bengaluru - 560 070, Karnataka, India. E-mail:
| | - P. A. Vijaya
- Department of Electronics and Communication Engineering, BNM Institute of Technology, Bangalore and Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India
| | - Ajay Kumar Nair
- Department of Neurophysiology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | - Bindu M. Kutty
- Department of Neurophysiology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| |
Collapse
|
9
|
Newberg AB, Wintering NA, Hriso C, Vedaei F, Stoner M, Ross R. Alterations in Functional Connectivity Measured by Functional Magnetic Resonance Imaging and the Relationship With Heart Rate Variability in Subjects After Performing Orgasmic Meditation: An Exploratory Study. Front Psychol 2021; 12:708973. [PMID: 34858249 PMCID: PMC8631761 DOI: 10.3389/fpsyg.2021.708973] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 10/15/2021] [Indexed: 12/21/2022] Open
Abstract
Background: We measured changes in resting brain functional connectivity, with blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), associated with a creative meditation practice that is augmented by clitoral stimulation and is designed to not only achieve a spiritual experience but to help individuals manage their most intimate personal relationships. Briefly, the meditative state is attained by both the male and female participants while the male stimulates the woman’s clitoris. The goal of this practice, called orgasmic meditation (OM), according to the practitioners is not sexual, but to use the focus on clitoral stimulation to facilitate a meditative state of connectedness and calm alertness between the two participants. Methods: fMRI was acquired on 20 pairs of subjects shortly following one of two states that were randomized in their order – during the OM practice or during a neutral condition. The practice is performed while the female is lying down on pillows with the clitoris exposed. During the practice, the male performs digital stimulation of the clitoris for 15 min. Resting BOLD image acquisition was performed at completion of the practice to assess changes in functional connectivity associated with the performance of the practice. Results: The results demonstrated significant changes (p < 0.05) in functional connectivity associated with the OM compared to the neutral condition. For the entire group there was altered connectivity following the OM practice involving the left superior temporal lobe, the frontal lobe, anterior cingulate, and insula. In female subjects, there was altered connectivity involving the cerebellum, thalamus, inferior frontal lobe posterior parietal lobe, angular gyrus, amygdala and middle temporal gyrus, and prefrontal cortex. In males, functional connectivity changes involved the supramarginal gyrus, cerebellum, and orbitofrontal gyrus, cerebellum, parahippocampus, inferior temporal gyrus, and anterior cingulate. Conclusion: Overall, these findings suggest a complex pattern of functional connectivity changes occurring in both members of the couple pair that result from this unique meditation practice. The changes represent a hybrid of functional connectivity findings with some similarities to meditation based practices and some with sexual stimulation and orgasm. This study has broader implications for understanding the dynamic relationship between sexuality and spirituality.
Collapse
Affiliation(s)
- Andrew B Newberg
- Department of Integrative Medicine and Nutritional Sciences, Thomas Jefferson University, Philadelphia, PA, United States.,Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Nancy A Wintering
- Department of Integrative Medicine and Nutritional Sciences, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chloe Hriso
- Department of Integrative Medicine and Nutritional Sciences, Thomas Jefferson University, Philadelphia, PA, United States
| | - Faezeh Vedaei
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Marie Stoner
- Department of Integrative Medicine and Nutritional Sciences, Thomas Jefferson University, Philadelphia, PA, United States
| | - Reneita Ross
- Department of Obstetrics and Gynecology, Thomas Jefferson University, Philadelphia, PA, United States
| |
Collapse
|
10
|
The Longitudinal Effect of Meditation on Resting-State Functional Connectivity Using Dynamic Arterial Spin Labeling: A Feasibility Study. Brain Sci 2021; 11:brainsci11101263. [PMID: 34679328 PMCID: PMC8533789 DOI: 10.3390/brainsci11101263] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/10/2021] [Accepted: 09/20/2021] [Indexed: 11/24/2022] Open
Abstract
We aimed to assess whether dynamic arterial spin labeling (dASL), a novel quantitative MRI technique with minimal contamination of subject motion and physiological noises, could detect the longitudinal effect of focused attention meditation (FAM) on resting-state functional connectivity (rsFC). A total of 10 novice meditators who recorded their FAM practice time were scanned at baseline and at the 2-month follow-up. Two-month meditation practice caused significantly increased rsFC between the left medial temporal (LMT) seed and precuneus area and between the right frontal eye (RFE) seed and medial prefrontal cortex. Meditation practice time was found to be positively associated with longitudinal changes of rsFC between the default mode network (DMN) and dorsal attention network (DAN), between DMN and insula, and between DAN and the frontoparietal control network (FPN) but negatively associated with changes of rsFC between DMN and FPN, and between DAN and visual regions. These findings demonstrate the capability of dASL in identifying the FAM-induced rsFC changes and suggest that the practice of FAM can strengthen the efficient control of FPN on fast switching between DMN and DAN and enhance the utilization of attentional resources with reduced focus on visual processing.
Collapse
|
11
|
Chen H, Guo Y, He Y, Ji J, Liu L, Shi Y, Wang Y, Yu L, Zhang X. Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity. Biostatistics 2021; 23:967-989. [PMID: 33769450 DOI: 10.1093/biostatistics/kxab007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 02/20/2021] [Accepted: 02/22/2021] [Indexed: 01/03/2023] Open
Abstract
Growing evidence has shown that the brain connectivity network experiences alterations for complex diseases such as Alzheimer's disease (AD). Network comparison, also known as differential network analysis, is thus particularly powerful to reveal the disease pathologies and identify clinical biomarkers for medical diagnoses (classification). Data from neurophysiological measurements are multidimensional and in matrix-form. Naive vectorization method is not sufficient as it ignores the structural information within the matrix. In the article, we adopt the Kronecker product covariance matrices framework to capture both spatial and temporal correlations of the matrix-variate data while the temporal covariance matrix is treated as a nuisance parameter. By recognizing that the strengths of network connections may vary across subjects, we develop an ensemble-learning procedure, which identifies the differential interaction patterns of brain regions between the case group and the control group and conducts medical diagnosis (classification) of the disease simultaneously. Simulation studies are conducted to assess the performance of the proposed method. We apply the proposed procedure to the functional connectivity analysis of an functional magnetic resonance imaging study on AD. The hub nodes and differential interaction patterns identified are consistent with existing experimental studies, and satisfactory out-of-sample classification performance is achieved for medical diagnosis of AD.
Collapse
Affiliation(s)
- Hao Chen
- School of Statistics, Shandong University of Finance and Economics, Jinan, 250014, China
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Yong He
- Institute for Financial Studies, Shandong University, Jinan, 250100, China
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, 250100, China
| | - Lei Liu
- Division of Biostatistics, Washington University in St.Louis, St. Louis, MO 63110, USA
| | - Yufeng Shi
- Institute for Financial Studies, Shandong University, Jinan, 250100, China
| | - Yikai Wang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Long Yu
- Department of Statistics, School of Management, Fudan University, Shanghai, 200433, China
| | - Xinsheng Zhang
- Department of Statistics, School of Management, Fudan University, Shanghai, 200433, China
| | | |
Collapse
|
12
|
Travis F. On the Neurobiology of Meditation: Comparison of Three Organizing Strategies to Investigate Brain Patterns during Meditation Practice. Medicina (B Aires) 2020; 56:medicina56120712. [PMID: 33353049 PMCID: PMC7767117 DOI: 10.3390/medicina56120712] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/28/2020] [Accepted: 12/16/2020] [Indexed: 11/17/2022] Open
Abstract
Three broad organizing strategies have been used to study meditation practices: (1) consider meditation practices as using similar processes and so combine neural images across a wide range of practices to identify the common underlying brain patterns of meditation practice, (2) consider meditation practices as unique and so investigate individual practices, or (3) consider meditation practices as fitting into larger categories and explore brain patterns within and between categories. The first organizing strategy combines meditation practices defined as deep concentration, attention to external and internal stimuli, and letting go of thoughts. Brain patterns of different procedures would all contribute to the final averages, which may not be representative of any practice. The second organizing strategy generates a multitude of brain patterns as each practice is studied individually. The rich detail of individual differences within each practice makes it difficult to identify reliable patterns between practices. The third organizing principle has been applied in three ways: (1) grouping meditations by their origin—Indian or Buddhist practices, (2) grouping meditations by the procedures of each practice, or (3) grouping meditations by brain wave frequencies reported during each practice. Grouping meditations by their origin mixes practices whose procedures include concentration, mindfulness, or effortless awareness, again resulting in a confounded pattern. Grouping meditations by their described procedures yields defining neural imaging patterns within each category, and clear differences between categories. Grouping meditations by the EEG frequencies associated with their procedures yields an objective system to group meditations and allows practices to “move” into different categories as subjects’ meditation experiences change over time, which would be associated with different brain patterns. Exploring meditations within theoretically meaningful categories appears to yield the most reliable picture of meditation practices.
Collapse
Affiliation(s)
- Frederick Travis
- Center for Brain, Consciousness and Cognition, Maharishi International University, Fairfield, IA 52557, USA
| |
Collapse
|
13
|
Knyazev GG, Savostyanov AN, Bocharov AV, Levin EA, Rudych PD. Intrinsic Connectivity Networks in the Self- and Other-Referential Processing. Front Hum Neurosci 2020; 14:579703. [PMID: 33304255 PMCID: PMC7693553 DOI: 10.3389/fnhum.2020.579703] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 10/05/2020] [Indexed: 12/26/2022] Open
Abstract
Neuroimaging studies have revealed a multitude of brain regions associated with self- and other-referential processing, but the question how the distinction between self, close other, and distant other is processed in the brain still remains unanswered. The default mode network (DMN) is the primary network associated with the processing of self, whereas task-positive networks (TPN) are indispensable for the processing of external objects. We hypothesize that self- and close-other-processing would engage DMN more than TPN, whereas distant-other-processing would engage TPN to a greater extent. To test this hypothesis, we used functional magnetic resonance imaging (fMRI) functional connectivity data obtained in the course of a trait adjective judgment task while subjects evaluated themselves, the best friend, a neutral stranger, and an unpleasant person. A positive association between the degree of self-relatedness and the degree of DMN dominance was revealed in cortical midline structures (CMS) and the left lateral prefrontal cortex. Relative to TPN, DMN showed greater connectivity in me than in friend, in friend than in stranger, and in stranger than in unpleasant conditions. These results show that the less the evaluated person is perceived as self-related, the more the balance of activity in the brain shifts from the DMN to the TPN.
Collapse
Affiliation(s)
- Gennady G Knyazev
- Laboratory of Psychophysiology of Individual Differences, Institute of Physiology and Basic Medicine, Novosibirsk, Russia
| | - Alexander N Savostyanov
- Laboratory of Psychophysiology of Individual Differences, Institute of Physiology and Basic Medicine, Novosibirsk, Russia.,Joint Laboratory of Psychological Genetics at the Institute of Cytology and Genetics SB RAS, Institute of Physiology and Basic Medicine, Novosibirsk, Russia
| | - Andrey V Bocharov
- Laboratory of Psychophysiology of Individual Differences, Institute of Physiology and Basic Medicine, Novosibirsk, Russia
| | - Evgeny A Levin
- E.N. Meshalkin National Medical Research Center, Novosibirsk, Russia
| | - Pavel D Rudych
- Laboratory of Psychophysiology of Individual Differences, Institute of Physiology and Basic Medicine, Novosibirsk, Russia
| |
Collapse
|
14
|
Tobe M, Saito S. Analogy between classical Yoga/Zen breathing and modern clinical respiratory therapy. J Anesth 2020; 34:944-949. [PMID: 32803435 PMCID: PMC7429199 DOI: 10.1007/s00540-020-02840-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 08/09/2020] [Indexed: 11/25/2022]
Abstract
Anesthesiologists and intensivists are modern-day professionals who provide appropriate respiratory care, vital for patient survival. Recently, anesthesiologists have increasingly focused their attention on the type of spontaneous breathing made by non-intubated patients with pulmonary disease cared for in an intensive care unit, and also patients with chronic pain receiving cognitive behavioral therapy. Prior to our modern understanding of respiratory physiology, Zen meditators recognized that breathing has a significant impact on a person’s mental state and general physical well-being. Examples of this knowledge regarding respiration include the beneficial effects of deep inhalation and slow exhalation on anxiety and general wellness. The classical literature has noted many suggestions for breathing and its psycho-physical effects. In the present review, we examine the effect of classical breathing methods and find an analogy between typical Yoga/Zen breathing and modern clinical respiratory therapy. Evidence is increasing about historical breathing and related meditation techniques that may be effective in modern clinical practice, especially in the field of anesthesiology, such as in improving respiratory function and reducing chronic pain. Clarification of the detailed mechanisms involved is anticipated.
Collapse
Affiliation(s)
- Masaru Tobe
- Department of Anesthesiology, Gunma University Graduate School of Medicine, 3-39-22, Showa, Maebashi, Gunma, 371-8511, Japan.
| | - Shigeru Saito
- Department of Anesthesiology, Gunma University Graduate School of Medicine, 3-39-22, Showa, Maebashi, Gunma, 371-8511, Japan
| |
Collapse
|
15
|
Lukemire J, Wang Y, Verma A, Guo Y. HINT: A hierarchical independent component analysis toolbox for investigating brain functional networks using neuroimaging data. J Neurosci Methods 2020; 341:108726. [PMID: 32360892 PMCID: PMC7338248 DOI: 10.1016/j.jneumeth.2020.108726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 03/13/2020] [Accepted: 04/06/2020] [Indexed: 11/18/2022]
Abstract
BACKGROUND Independent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain networks are modulated by subjects' clinical and demographic variables. Existing ICA methods and toolboxes don't incorporate subjects' covariates effects in ICA estimation of brain networks, which potentially leads to loss in accuracy and statistical power in detecting brain network differences between subjects' groups. NEW METHOD We introduce a Matlab toolbox, HINT (Hierarchical INdependent component analysis Toolbox), that provides a hierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariate effects and generates model-based estimates of brain networks on both the population- and individual-level. HINT provides a user-friendly Matlab GUI that allows users to easily load images, specify covariate effects, monitor model estimation via an EM algorithm, specify hypothesis tests, and visualize results. HINT also has a command line interface which allows users to conveniently run and reproduce the analysis with a script. COMPARISON TO EXISTING METHODS HINT implements a new multi-level probabilistic ICA model for group ICA. It provides a statistically principled ICA modeling framework for investigating covariate effects on brain networks. HINT can also generate and visualize model-based network estimates for user-specified subject groups, which greatly facilitates group comparisons. RESULTS We demonstrate the steps and functionality of HINT with an fMRI example data to estimate treatment effects on brain networks while controlling for other covariates. Results demonstrate estimated brain networks and model-based comparisons between the treatment and control groups. In comparisons using synthetic fMRI data, HINT shows desirable statistical power in detecting group differences in networks especially in small sample sizes, while maintaining a low false positive rate. HINT also demonstrates similar or increased accuracy in reconstructing both population- and individual-level source signal maps as compared to some state-of-the-art group ICA methods. CONCLUSION HINT can provide a useful tool for both statistical and neuroscience researchers to evaluate and test differences in brain networks between subject groups.
Collapse
Affiliation(s)
- Joshua Lukemire
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yikai Wang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Amit Verma
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
| |
Collapse
|
16
|
Kundu S, Lukemire J, Wang Y, Guo Y. A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data. Sci Rep 2019; 9:19589. [PMID: 31863067 PMCID: PMC6925181 DOI: 10.1038/s41598-019-55818-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 11/26/2019] [Indexed: 12/14/2022] Open
Abstract
There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility.
Collapse
Affiliation(s)
- Suprateek Kundu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA.
| | - Joshua Lukemire
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
| | - Yikai Wang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Ga, 30322, USA
| |
Collapse
|
17
|
From State-to-Trait Meditation: Reconfiguration of Central Executive and Default Mode Networks. eNeuro 2019; 6:ENEURO.0335-18.2019. [PMID: 31694816 PMCID: PMC6893234 DOI: 10.1523/eneuro.0335-18.2019] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 10/03/2019] [Accepted: 10/08/2019] [Indexed: 12/17/2022] Open
Abstract
While brain default mode network (DMN) activation in human subjects has been associated with mind wandering, meditation practice has been found to suppress it and to increase psychological well-being. In addition to DMN activity reduction, experienced meditators (EMs) during meditation practice show an increased connectivity between the DMN and the central executive network (CEN). While brain default mode network (DMN) activation in human subjects has been associated with mind wandering, meditation practice has been found to suppress it and to increase psychological well-being. In addition to DMN activity reduction, experienced meditators (EMs) during meditation practice show an increased connectivity between the DMN and the central executive network (CEN). However, the gradual change between DMN and CEN configuration from pre-meditation, during meditation, and post-meditation is unknown. Here, we investigated the change in DMN and CEN configuration by means of brain activity and functional connectivity (FC) analyses in EMs across three back-to-back functional magnetic resonance imaging (fMRI) scans: pre-meditation baseline (trait), meditation (state), and post-meditation (state-to-trait). Pre-meditation baseline group comparison was also performed between EMs and healthy controls (HCs). Meditation trait was characterized by a significant reduction in activity and FC within DMN and increased anticorrelations between DMN and CEN. Conversely, meditation state and meditation state-to-trait periods showed increased activity and FC within the DMN and between DMN and CEN. However, the latter anticorrelations were only present in EMs with limited practice. The interactions between networks during these states by means of positive diametric activity (PDA) of the fractional amplitude of low-frequency fluctuations (fALFFs) defined as CEN fALFF¯ − DMN fALFF¯ revealed no trait differences but significant increases during meditation state that persisted in meditation state-to-trait. The gradual reconfiguration in DMN and CEN suggest a neural mechanism by which the CEN negatively regulates the DMN and is probably responsible for the long-term trait changes seen in meditators and reported psychological well-being.
Collapse
|
18
|
Guidolin D, Marcoli M, Tortorella C, Maura G, Agnati LF. From the hierarchical organization of the central nervous system to the hierarchical aspects of biocodes. Biosystems 2019; 183:103975. [PMID: 31128147 DOI: 10.1016/j.biosystems.2019.103975] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 12/17/2022]
Abstract
The quite recent (at least on the evolutionary time scale) emergence of nervous systems in complex organisms enabled the living beings to build a wide-ranging model of the external world in order to predict and evaluate the outcomes of their actions. Such a process likely represents a real coding activity, since, by proper handling of information, it generates a mapping between the external environment and internal cerebral activity patterns. The patterns of neural activity that correspond to the final maps, however, emerge from the holistic assembly of a multilevel functional organization. Nerve tissue components, indeed, appear organized in compartments, also called functional modules (FM), that contain system components and circuits of different miniaturizations not only arranged to work together either in parallel or in series but also nested within each other. At least three levels can be recognized in a functional module and it is possible to point out that such a hierarchical organization of the brain circuits could be mirrored by a corresponding hierarchical organization of biocodes. This feature can also suggest the hypothesis that the same logic could operate also at system level to integrate FM into functional brain areas and to associate areas to generate the final map used by humans to image the external world and to imagine untestable worlds.
Collapse
Affiliation(s)
- D Guidolin
- Department of Neuroscience, Section of Anatomy, University of Padova, via Gabelli 65, 35121 Padova, Italy.
| | - M Marcoli
- Department of Pharmacy, Section of Pharmacology and Toxicology, University of Genova, Viale Cembrano 4, 16148, Genova, Italy
| | - C Tortorella
- Department of Neuroscience, Section of Anatomy, University of Padova, via Gabelli 65, 35121 Padova, Italy
| | - G Maura
- Department of Pharmacy, Section of Pharmacology and Toxicology, University of Genova, Viale Cembrano 4, 16148, Genova, Italy
| | - L F Agnati
- Department of Diagnostic, Clinical Medicine and Public Health, University of Modena and Reggio Emilia, Via Campi 287, 41125, Modena, Italy; Department of Neuroscience, Karolinska Institutet, Retzius väg 8, Stockholm, Sweden
| |
Collapse
|
19
|
Wang Y, Guo Y. A hierarchical independent component analysis model for longitudinal neuroimaging studies. Neuroimage 2019; 189:380-400. [PMID: 30639837 PMCID: PMC6422710 DOI: 10.1016/j.neuroimage.2018.12.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 10/15/2018] [Accepted: 12/11/2018] [Indexed: 01/10/2023] Open
Abstract
In recent years, longitudinal neuroimaging study has become increasingly popular in neuroscience research to investigate disease-related changes in brain functions, to study neurodevelopment or to evaluate treatment effects on neural processing. One of the important goals in longitudinal imaging analysis is to study changes in brain functional networks across time and how the changes are modulated by subjects' clinical or demographic variables. In current neuroscience literature, one of the most commonly used tools to extract and characterize brain functional networks is independent component analysis (ICA), which separates multivariate signals into linear mixture of independent components. However, existing ICA methods are only applicable to cross-sectional studies and not suited for modeling repeatedly measured imaging data. In this paper, we propose a novel longitudinal independent component model (L-ICA) which provides a formal modeling framework for extending ICA to longitudinal studies. By incorporating subject-specific random effects and visit-specific covariate effects, L-ICA is able to provide more accurate estimates of changes in brain functional networks on both the population- and individual-level, borrow information across repeated scans within the same subject to increase statistical power in detecting covariate effects on the networks, and allow for model-based prediction for brain networks changes caused by disease progression, treatment or neurodevelopment. We develop a fully traceable exact EM algorithm to obtain maximum likelihood estimates of L-ICA. We further develop a subspace-based approximate EM algorithm which greatly reduce the computation time while still retaining high accuracy. Moreover, we present a statistical testing procedure for examining covariate effects on brain network changes. Simulation results demonstrate the advantages of our proposed methods. We apply L-ICA to ADNI2 study to investigate changes in brain functional networks in Alzheimer disease. Results from the L-ICA provide biologically insightful findings which are not revealed using existing methods.
Collapse
Affiliation(s)
- Yikai Wang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton rd., Atlanta, 30322, Georgia, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton rd., Atlanta, 30322, Georgia, USA.
| |
Collapse
|
20
|
Deepeshwar S, Nagendra HR, Rana BB, Visweswaraiah NK. Evolution from four mental states to the highest state of consciousness: A neurophysiological basis of meditation as defined in yoga texts. PROGRESS IN BRAIN RESEARCH 2019; 244:31-83. [PMID: 30732843 DOI: 10.1016/bs.pbr.2018.10.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
This chapter provides a theoretical introduction to states of consciousness and reviews neuroscientific investigations of meditation. The different states of consciousness consist of four mental states, i.e., cancalata (random thinking), ekagrata (non-meditative focusing), dharna (focused meditation), and dhyana (meditation) as defined in yoga texts. Meditation is a self-regulated mental process associated with deep relaxation and increased internalized attention. Scientific investigations on meditation reported changes in electrophysiological signals and neuroimaging measures. But most outcomes of meditation studies showed inconsistent results, this may be due to heterogeneity in meditation methods and techniques evolved in the last 200 years. Traditionally, the features of meditation include the capacity to sustain a heightened awareness of thoughts, behaviors, emotions, and perceptions. Generally, meditation involves non-reactive effortless monitoring of the content of experience from moment to moment. Focused meditation practice involves awareness on a single object and open monitoring meditation is a non-directive meditation involved attention in breathing, mantra, or sound. Therefore, results of few empirical studies of advanced meditators or beginners remain tentative. This is an attempt to compile the meditation-related changes in electrophysiological and neuroimaging processes among experienced and novice practitioners.
Collapse
Affiliation(s)
- Singh Deepeshwar
- Department of Yoga and Life Sciences, Cognitive Neuroscience Lab, Swami Vivekananda Yoga University (S-VYASA), Bengaluru, India
| | - H R Nagendra
- Department of Yoga and Life Sciences, Cognitive Neuroscience Lab, Swami Vivekananda Yoga University (S-VYASA), Bengaluru, India
| | - Bal Budhi Rana
- Department of Yoga and Life Sciences, Cognitive Neuroscience Lab, Swami Vivekananda Yoga University (S-VYASA), Bengaluru, India
| | | |
Collapse
|
21
|
Kemmer PB, Wang Y, Bowman FD, Mayberg H, Guo Y. Evaluating the Strength of Structural Connectivity Underlying Brain Functional Networks. Brain Connect 2018. [DOI: 10.1089/brain.2018.0615] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Phebe Brenne Kemmer
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Yikai Wang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - F. DuBois Bowman
- University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Helen Mayberg
- Departments of Psychiatry and Neurology, Emory University School of Medicine, Atlanta, Georgia
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
| |
Collapse
|
22
|
On the relation between theory of mind and executive functioning: A developmental cognitive neuroscience perspective. Psychon Bull Rev 2018; 25:2119-2140. [DOI: 10.3758/s13423-018-1459-0] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
23
|
Gundel F, von Spee J, Schneider S, Haeussinger FB, Hautzinger M, Erb M, Fallgatter AJ, Ehlis AC. Meditation and the brain - Neuronal correlates of mindfulness as assessed with near-infrared spectroscopy. Psychiatry Res Neuroimaging 2018; 271:24-33. [PMID: 28689600 DOI: 10.1016/j.pscychresns.2017.04.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 03/01/2017] [Accepted: 04/07/2017] [Indexed: 12/28/2022]
Abstract
Mindfulness meditation as a therapeutic intervention has been shown to have positive effects on psychological problems such as depression, pain or anxiety disorders. In this study, we used functional near-infrared spectroscopy (fNIRS) to detect differences in hemodynamic responses of meditation experts (14 participants) and a control group (16 participants) in a resting and a mindfulness condition. In both conditions, the sound of a meditation bowl was used to find group differences in the auditory system and adjacent cortical areas. Different lateralization patterns of the brain were found in expert meditators while being in a resting state (amplified left hemisphere) or being in mindfulness state (amplified right hemisphere). Compared to the control group, meditation experts had a more widespread pattern of activation in the auditory cortex, while resting. In the mindfulness condition, the control group showed a decrease of activation in higher auditory areas (BA 1, 6 and 40), whereas the meditation experts had a significant increase in those areas. In addition, meditation expert had highly activated brain areas (BA 39, 40, 44 and 45) beyond the meditative task itself, indicating possible long-term changes in the brain and their positive effects on empathy, meta cognitive skills and health.
Collapse
Affiliation(s)
- Friederike Gundel
- Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstr. 14, 72076 Tuebingen, Germany.
| | - Johanna von Spee
- Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstr. 14, 72076 Tuebingen, Germany
| | - Sabrina Schneider
- Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstr. 14, 72076 Tuebingen, Germany
| | - Florian B Haeussinger
- Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstr. 14, 72076 Tuebingen, Germany
| | - Martin Hautzinger
- Department of Psychology, University of Tuebingen, Schleichstr. 4, 72076 Tuebingen, Germany
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstr. 14, 72076 Tuebingen, Germany; LEAD Graduate School & Research Network, Gartenstraße 29, 72074 Tübingen, Germany; Centre for Integrative Neuroscience, Otfried-Müller-Str. 25, 72076 Tübingen, Germany
| | - Ann-Christine Ehlis
- Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstr. 14, 72076 Tuebingen, Germany; LEAD Graduate School & Research Network, Gartenstraße 29, 72074 Tübingen, Germany
| |
Collapse
|
24
|
Herrero JL, Khuvis S, Yeagle E, Cerf M, Mehta AD. Breathing above the brain stem: volitional control and attentional modulation in humans. J Neurophysiol 2017; 119:145-159. [PMID: 28954895 DOI: 10.1152/jn.00551.2017] [Citation(s) in RCA: 168] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Whereas the neurophysiology of respiration has traditionally focused on automatic brain stem processes, higher brain mechanisms underlying the cognitive aspects of breathing are gaining increasing interest. Therapeutic techniques have used conscious control and awareness of breathing for millennia with little understanding of the mechanisms underlying their efficacy. Using direct intracranial recordings in humans, we correlated cortical and limbic neuronal activity as measured by the intracranial electroencephalogram (iEEG) with the breathing cycle. We show this to be the direct result of neuronal activity, as demonstrated by both the specificity of the finding to the cortical gray matter and the tracking of breath by the gamma-band (40-150 Hz) envelope in these structures. We extend prior observations by showing the iEEG signal to track the breathing cycle across a widespread network of cortical and limbic structures. We further demonstrate a sensitivity of this tracking to cognitive factors by using tasks adapted from cognitive behavioral therapy and meditative practice. Specifically, volitional control and awareness of breathing engage distinct but overlapping brain circuits. During volitionally paced breathing, iEEG-breath coherence increases in a frontotemporal-insular network, and during attention to breathing, we demonstrate increased coherence in the anterior cingulate, premotor, insular, and hippocampal cortices. Our findings suggest that breathing can act as an organizing hierarchical principle for neuronal oscillations throughout the brain and detail mechanisms of how cognitive factors impact otherwise automatic neuronal processes during interoceptive attention. NEW & NOTEWORTHY Whereas the link between breathing and brain activity has a long history of application to therapy, its neurophysiology remains unexplored. Using intracranial recordings in humans, we show neuronal activity to track the breathing cycle throughout widespread cortical/limbic sites. Volitional pacing of the breath engages frontotemporal-insular cortices, whereas attention to automatic breathing modulates the cingulate cortex. Our findings imply a fundamental role of breathing-related oscillations in driving neuronal activity and provide insight into the neuronal mechanisms of interoceptive attention.
Collapse
Affiliation(s)
- Jose L Herrero
- The Feinstein Institute for Medical Research, Manhasset, New York.,Department of Neurosurgery, Hofstra Northwell School of Medicine, Manhasset, New York
| | - Simon Khuvis
- The Feinstein Institute for Medical Research, Manhasset, New York.,Department of Neurosurgery, Hofstra Northwell School of Medicine, Manhasset, New York
| | - Erin Yeagle
- The Feinstein Institute for Medical Research, Manhasset, New York.,Department of Neurosurgery, Hofstra Northwell School of Medicine, Manhasset, New York
| | - Moran Cerf
- Interdepartmental Neuroscience Program and Kellogg School of Management, Northwestern University , Evanston, Illinois
| | - Ashesh D Mehta
- The Feinstein Institute for Medical Research, Manhasset, New York.,Department of Neurosurgery, Hofstra Northwell School of Medicine, Manhasset, New York
| |
Collapse
|
25
|
Vago DR, Zeidan F. The brain on silent: mind wandering, mindful awareness, and states of mental tranquility. Ann N Y Acad Sci 2017; 1373:96-113. [PMID: 27398642 DOI: 10.1111/nyas.13171] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 06/08/2016] [Accepted: 06/13/2016] [Indexed: 12/27/2022]
Abstract
Mind wandering and mindfulness are often described as divergent mental states with opposing effects on cognitive performance and mental health. Spontaneous mind wandering is typically associated with self-reflective states that contribute to negative processing of the past, worrying/fantasizing about the future, and disruption of primary task performance. On the other hand, mindful awareness is frequently described as a focus on present sensory input without cognitive elaboration or emotional reactivity, and is associated with improved task performance and decreased stress-related symptomology. Unfortunately, such distinctions fail to acknowledge similarities and interactions between the two states. Instead of an inverse relationship between mindfulness and mind wandering, a more nuanced characterization of mindfulness may involve skillful toggling back and forth between conceptual and nonconceptual processes and networks supporting each state, to meet the contextually specified demands of the situation. In this article, we present a theoretical analysis and plausible neurocognitive framework of the restful mind, in which we attempt to clarify potentially adaptive contributions of both mind wandering and mindful awareness through the lens of the extant neurocognitive literature on intrinsic network activity, meditation, and emerging descriptions of stillness and nonduality. A neurophenomenological approach to probing modality-specific forms of concentration and nonconceptual awareness is presented that may improve our understanding of the resting state. Implications for future research are discussed.
Collapse
Affiliation(s)
- David R Vago
- Functional Neuroimaging Laboratory, Brigham & Women's Hospital and Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Fadel Zeidan
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, North Carolina
| |
Collapse
|
26
|
Aso T, Nishimura K, Kiyonaka T, Aoki T, Inagawa M, Matsuhashi M, Tobinaga Y, Fukuyama H. Dynamic interactions of the cortical networks during thought suppression. Brain Behav 2016; 6:e00503. [PMID: 27547504 PMCID: PMC4980473 DOI: 10.1002/brb3.503] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 04/06/2016] [Accepted: 05/03/2016] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES Thought suppression has spurred extensive research in clinical and preclinical fields, particularly with regard to the paradoxical aspects of this behavior. However, the involvement of the brain's inhibitory system in the dynamics underlying the continuous effort to suppress thoughts has yet to be clarified. This study aims to provide a unified perspective for the volitional suppression of internal events incorporating the current understanding of the brain's inhibitory system. MATERIALS AND METHODS Twenty healthy volunteers underwent functional magnetic resonance imaging while they performed thought suppression blocks alternating with visual imagery blocks. The whole dataset was decomposed by group-independent component analysis into 30 components. After discarding noise components, the 20 valid components were subjected to further analysis of their temporal properties including task-relatedness and between-component residual correlation. RESULTS Combining a long task period and a data-driven approach, we observed a right-side-dominant, lateral frontoparietal network to be strongly suppression related. This network exhibited increased fluctuation during suppression, which is compatible with the well-known difficulty of suppression maintenance. CONCLUSIONS Between-network correlation provided further insight into the coordinated engagement of the executive control and dorsal attention networks, as well as the reciprocal activation of imagery-related components, thus revealing neural substrates associated with the rivalry between intrusive thoughts and the suppression process.
Collapse
Affiliation(s)
- Toshihiko Aso
- Human Brain Research CenterKyoto University Graduate School of MedicineKyotoJapan
| | | | - Takashi Kiyonaka
- Human Brain Research CenterKyoto University Graduate School of MedicineKyotoJapan
| | - Takaaki Aoki
- Institute of Economic ResearchKyoto UniversityKyotoJapan
| | | | - Masao Matsuhashi
- Human Brain Research CenterKyoto University Graduate School of MedicineKyotoJapan
| | | | - Hidenao Fukuyama
- Human Brain Research CenterKyoto University Graduate School of MedicineKyotoJapan
| |
Collapse
|
27
|
Berkovich-Ohana A, Harel M, Hahamy A, Arieli A, Malach R. Alterations in task-induced activity and resting-state fluctuations in visual and DMN areas revealed in long-term meditators. Neuroimage 2016; 135:125-34. [DOI: 10.1016/j.neuroimage.2016.04.024] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Revised: 03/21/2016] [Accepted: 04/11/2016] [Indexed: 12/19/2022] Open
|
28
|
Mooneyham BW, Mrazek MD, Mrazek AJ, Schooler JW. Signal or noise: brain network interactions underlying the experience and training of mindfulness. Ann N Y Acad Sci 2016; 1369:240-56. [PMID: 27038003 DOI: 10.1111/nyas.13044] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 02/12/2016] [Accepted: 02/22/2016] [Indexed: 11/28/2022]
Abstract
A broad set of brain regions has been associated with the experience and training of mindfulness. Many of these regions lie within key intrinsic brain networks, including the executive control, salience, and default networks. In this paper, we review the existing literature on the cognitive neuroscience of mindfulness through the lens of network science. We describe the characteristics of the intrinsic brain networks implicated in mindfulness and summarize the relevant findings pertaining to changes in functional connectivity (FC) within and between these networks. Convergence across these findings suggests that mindfulness may be associated with increased FC between two regions within the default network: the posterior cingulate cortex and the ventromedial prefrontal cortex. Additionally, extensive meditation experience may be associated with increased FC between the insula and the dorsolateral prefrontal cortex. However, little consensus has emerged within the existing literature owing to the diversity of operational definitions of mindfulness, neuroimaging methods, and network characterizations. We describe several challenges to develop a coherent cognitive neuroscience of mindfulness and to provide detailed recommendations for future research.
Collapse
Affiliation(s)
- Benjamin W Mooneyham
- Department of Psychological & Brain Sciences, University of California Santa Barbara, Santa Barbara, California
| | - Michael D Mrazek
- Department of Psychological & Brain Sciences, University of California Santa Barbara, Santa Barbara, California
| | - Alissa J Mrazek
- Department of Psychology, Northwestern University, Evanston, Illinois
| | - Jonathan W Schooler
- Department of Psychological & Brain Sciences, University of California Santa Barbara, Santa Barbara, California
| |
Collapse
|
29
|
Wang Y, Kang J, Kemmer PB, Guo Y. An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation. Front Neurosci 2016; 10:123. [PMID: 27242395 PMCID: PMC4876368 DOI: 10.3389/fnins.2016.00123] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 03/13/2016] [Indexed: 01/22/2023] Open
Abstract
Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package “DensParcorr” can be downloaded from CRAN for implementing the proposed statistical methods.
Collapse
Affiliation(s)
- Yikai Wang
- Department of Biostatistics and Bioinformatics, The Rollins School of Public Health, Emory University Atlanta, GA, USA
| | - Jian Kang
- Department of Biostatistics, School of Public Health, University of Michigan Ann Arbor, MI, USA
| | - Phebe B Kemmer
- Department of Biostatistics and Bioinformatics, The Rollins School of Public Health, Emory University Atlanta, GA, USA
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, The Rollins School of Public Health, Emory University Atlanta, GA, USA
| |
Collapse
|
30
|
Yang Z, Zuo XN, McMahon KL, Craddock RC, Kelly C, de Zubicaray GI, Hickie I, Bandettini PA, Castellanos FX, Milham MP, Wright MJ. Genetic and Environmental Contributions to Functional Connectivity Architecture of the Human Brain. Cereb Cortex 2016; 26:2341-2352. [PMID: 26891986 PMCID: PMC4830303 DOI: 10.1093/cercor/bhw027] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
One of the grand challenges faced by neuroscience is to delineate the determinants of interindividual variation in the comprehensive structural and functional connection matrices that comprise the human connectome. At present, this endeavor appears most tractable at the macroanatomic scale, where intrinsic brain activity exhibits robust patterns of synchrony that recapitulate core functional circuits at the individual level. Here, we use a classical twin study design to examine the heritability of intrinsic functional network properties in 101 twin pairs, including network activity (i.e., variance of a network's specific temporal fluctuations) and internetwork coherence (i.e., correlation between networks' specific temporal fluctuations). Five of 7 networks exhibited significantly heritable (23.3–65.2%) network activity, 6 of the 21 internetwork coherences were significantly heritable (25.6–42.0%), and 11 of the 21 internetwork coherences were significantly influenced by common environmental factors (18.0–47.1%). These results suggest that the source of interindividual variation in functional connectome has a modular architecture: individual modules represented by intrinsic connectivity networks are genetic controlled, while environmental factors influence the interplays between the modules. This work further provides network-specific hypotheses for discovery of the specific genetic and environmental factors influencing functional specialization and integration of the human brain.
Collapse
Affiliation(s)
- Zhi Yang
- Key Laboratory of Behavioral Sciences and MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Xi-Nian Zuo
- Key Laboratory of Behavioral Sciences and MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Katie L McMahon
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - R Cameron Craddock
- Child Mind Institute, New York, NY, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Clare Kelly
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York, NY, USA
| | | | - Ian Hickie
- Brain and Mind Research Institute, University of Sydney, Sydney, Australia
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - F Xavier Castellanos
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York, NY, USA
| | - Michael P Milham
- Child Mind Institute, New York, NY, USA.,Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Margaret J Wright
- Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, QLD, Australia
| |
Collapse
|
31
|
Tomasino B, Fabbro F. Editorial: Neuroimaging and Neuropsychology of Meditation States. Front Psychol 2015; 6:1757. [PMID: 26635667 PMCID: PMC4652575 DOI: 10.3389/fpsyg.2015.01757] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 11/02/2015] [Indexed: 12/28/2022] Open
Affiliation(s)
| | - Franco Fabbro
- Department of Medical and Biological Sciences, University of Udine Udine, Italy ; Perceptual Robotics Laboratory, Sant'Anna School of Advanced Studies Pisa, Italy
| |
Collapse
|