scholarly journals A Review of the Methodology, Taxonomy, and Definitions in Recent fMRI Research on Meditation

Mindfulness ◽  
2021 ◽  
Author(s):  
Maria Engström ◽  
Johan Willander ◽  
Rozalyn Simon
Keyword(s):  
Brain Function ◽  
Subjective Experience ◽  
Good Health ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Present Moment ◽  
Inclusion Criteria ◽  
Potential Improvement ◽  
Promotion Of Good Health ◽  
Healthy Participants

Abstract Objectives As meditation is increasingly employed for the promotion of good health, there is a growing interest in using neuroimaging methods to investigate the neural mechanisms by which meditation acts. In the wake of this rising interest, criticism regarding the lack of clarity concerning theory, definitions, and taxonomy, as well as deficient or poorly reported methodology, has arisen. The aim of this study was to investigate trends in current neuroimaging research on meditation and to provide guidelines for future studies. Methods We made a literature search for articles published during 2016–2019 using the search phrases “meditation” and “functional magnetic resonance imaging or fMRI”. Inclusion criteria were limited to meditation studies using resting-state fMRI or such task-based fMRI examinations that were specifically targeting meditative states in healthy participants. Text analysis was performed using Nvivo 12 Mac (QSR International). Results Twenty-eight articles were included from which we identified four different intention-based dimensions of meditation practice: The present moment, Wholesome qualities to cultivate, Unwholesome qualities to avoid, and Attitudes. Half of the studies do not make assessments of subjective experience. The results were related to networks and brain regions describing cognitive, affective, somatic, and self domains of brain function. Most studies describe meditation-related brain function in terms of “processes”. Conclusions We defined five areas of potential improvement regarding research methodology: (1) Provide clear and unambiguous definitions of constructs and practices, (2) Include measures of subjective experience, (3) Perform correct assessment of processes, (4) Combine methodologies for more substantiated conclusions, (5) Avoid the risk of overinterpretation.

scholarly journals Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability

2016 ◽  
Author(s):  
Ting Xu ◽  
Alexander Opitz ◽  
R. Cameron Craddock ◽  
Margaret Wright ◽  
Xi-Nian Zuo ◽  
...  
Keyword(s):  
Individual Variation ◽  
Brain Function ◽  
Intraclass Correlation ◽  
Resting State Fmri ◽  
Single Individual ◽  
Initial Application ◽  
Novel Approach ◽  
Gradient Based ◽  
Healthy Participants

AbstractResting state fMRI (R-fMRI) is a powerful in-vivo tool for examining the functional architecture of the human brain. Recent studies have demonstrated the ability to characterize transitions between functionally distinct cortical areas through the mapping of gradients in intrinsic functional connectivity (iFC) profiles. To date, this novel approach has primarily been applied to iFC profiles averaged across groups of individuals, or in one case, a single individual scanned multiple times. Here, we used a publically available R-fMRI dataset, in which 30 healthy participants were scanned 10 times (10 minutes per session), to investigate differences in full-brain transition profiles (i.e., gradient maps, edge maps) across individuals, and their reliability. 10-minute R-fMRI scans were sufficient to achieve high accuracies in efforts to “fingerprint” individuals based upon full-brain transition profiles. Regarding testretest reliability, the image-wise intraclass correlation coefficient (ICC) was moderate, and vertex-level ICC varied depending on region; larger durations of data yielded higher reliability scores universally. Initial application of gradient-based methodologies to a recently published dataset obtained from twins suggested inter-individual variation in areal profiles might have genetic and familial origins. Overall, these results illustrate the utility of gradient-based iFC approaches for studying inter-individual variation in brain function.

scholarly journals Alteration of Power Law Scaling of Spontaneous Brain Activity in Schizophrenia

2020 ◽  
Author(s):  
Yi-Ju Lee ◽  
Su-Yun Huang ◽  
Ching-Po Lin ◽  
Shih-Jen Tsai ◽  
Albert C. Yang
Keyword(s):  
Mental Illness ◽  
Power Law ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Psychotic Symptoms ◽  
Positive Power ◽  
Fmri Signal ◽  
Frequency Components ◽  
Healthy Participants

AbstractNonlinear dynamical analysis has been used to quantify the complexity of brain signal at temporal scales. Power law scaling is a well-validated method in physics that has been used to describe the complex nature of a system across different time scales. In this research, we investigated the change of power-law characteristics in a large-scale resting-state fMRI data of schizophrenia (N = 200) and healthy participants (N = 200) derived from Taiwan Aging and Mental Illness cohort. Fourier transform was used to determine the power spectral density (PSD) of resting-state fMRI signal. We estimated the power law scaling of PSD of resting-state fMRI signal by determining the slope of the regression line fitting to the log-log plot of PSD. The power law scaling represents the dynamical properties of resting-state fMRI signal ranging from noisy oscillation (e.g., white noise) to complex fluctuations (e.g., slope approaches −1). Linear regression model was used to assess the statistical difference in power law scaling between schizophrenia and healthy participants. The significant differences in power law scaling were found in six brain regions. Schizophrenia patients has significantly more positive power law scaling (i.e., frequency components become more homogenous) at four brain regions: left precuneus, left medial dorsal nucleus, right inferior frontal gyrus, and right middle temporal gyrus, compared with healthy participants. Additionally, schizophrenia exhibited less positive power law scaling (i.e., frequency components are more dominant at lower frequency range) in bilateral putamen. Significant correlations of power law scaling with the severity of psychosis were found in these identified brain areas in schizophrenia. These findings suggest that schizophrenia has abnormal brain signal complexity toward random patterns, which is linked to psychotic symptoms. The power law scaling analysis may serve as a novel functional brain imaging marker for evaluating patients with mental illness.

PHASE SYNCHRONIZATION IN BRAIN NETWORKS DERIVED FROM CORRELATION BETWEEN PROBABILITIES OF RECURRENCES IN FUNCTIONAL MRI DATA

2013 ◽  
Vol 23 (02) ◽  
pp. 1350003 ◽  
Author(s):  
D. RANGAPRAKASH ◽  
XIAOPING HU ◽  
GOPIKRISHNA DESHPANDE
Keyword(s):  
Linear Correlation ◽  
Resting State ◽  
Brain Function ◽  
Phase Synchronization ◽  
Human Subjects ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fmri Data

It is increasingly being recognized that resting state brain connectivity derived from functional magnetic resonance imaging (fMRI) data is an important marker of brain function both in healthy and clinical populations. Though linear correlation has been extensively used to characterize brain connectivity, it is limited to detecting first order dependencies. In this study, we propose a framework where in phase synchronization (PS) between brain regions is characterized using a new metric "correlation between probabilities of recurrence" (CPR) and subsequent graph-theoretic analysis of the ensuing networks. We applied this method to resting state fMRI data obtained from human subjects with and without administration of propofol anesthetic. Our results showed decreased PS during anesthesia and a biologically more plausible community structure using CPR rather than linear correlation. We conclude that CPR provides an attractive nonparametric method for modeling interactions in brain networks as compared to standard correlation for obtaining physiologically meaningful insights about brain function.

Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia

2021 ◽  
pp. 1-14
Author(s):  
Debo Dong ◽  
Dezhong Yao ◽  
Yulin Wang ◽  
Seok-Jun Hong ◽  
Sarah Genon ◽  
...  
Keyword(s):  
Sensory Information ◽  
Resting State Fmri ◽  
Brain Regions ◽  
High Order ◽  
Hierarchical Organization ◽  
System Level ◽  
Integrative System ◽  
Sensorimotor Region ◽  
Cortical Hierarchy ◽  
High Level

Abstract Background Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Methods We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). Results We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal−parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). Conclusions The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory−motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.

Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

2021 ◽  
pp. 1-29
Author(s):  
Kangyu Jin ◽  
Zhe Shen ◽  
Guoxun Feng ◽  
Zhiyong Zhao ◽  
Jing Lu ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Resting State ◽  
Parietal Lobe ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Regional Homogeneity ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Fmri Study

Abstract Objective: A few former studies suggested there are partial overlaps in abnormal brain structure and cognitive function between Hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear. Methods: 21 HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent Resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ. Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL) and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG and left paracentral lobule but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MCCB in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients. Conclusion: our results suggested the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.

scholarly journals Critical health response in the time of COVID-19: lessons from the past

2021 ◽  
Vol 51 (2) ◽  
pp. 336-345
Author(s):  
Rabia Ruby Patel ◽  
Tanya Monique Graham
Keyword(s):  
South African ◽  
Behaviour Change ◽  
Health Behaviour ◽  
Subjective Experience ◽  
Personal Responsibility ◽  
Good Health ◽  
Community Empowerment ◽  
The South ◽  
Change Models ◽  
Health Behaviour Change

This article examines the South African government’s response to COVID-19 by exploring the strong emphasis that has been placed on South Africans taking personal responsibility for good health outcomes. This emphasis is based on the principles of the traditional Health Belief Model which is a commonly used model in global health systems. More recently, there has been a drive towards other health behaviour change models, like the COM-B model and Behaviour Change Wheel (BCW); nonetheless, these remain entrenched within the principles of individual health responsibility. However, the South African experience with the HIV epidemic serves as a backdrop to demonstrate that holding people personally accountable for health behaviour changes has major pitfalls; health risk is never objective and does not take place outside of subjective experience. This article makes the argument that risk-taking health behaviour change in the South African context has to consider community empowerment and capacity building.

A rigorous approach for testing the constructionist hypotheses of brain function

2012 ◽  
Vol 35 (3) ◽  
pp. 148-149 ◽  
Author(s):  
Gopikrishna Deshpande ◽  
K. Sathian ◽  
Xiaoping Hu ◽  
Joseph A. Buckhalt
Keyword(s):  
Granger Causality ◽  
Strong Evidence ◽  
Bayesian Modeling ◽  
Brain Function ◽  
Brain Regions ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Rigorous Approach ◽  
Target Article ◽  
Co Activation

AbstractAlthough the target article provides strong evidence against the locationist view, evidence for the constructionist view is inconclusive, because co-activation of brain regions does not necessarily imply connectivity between them. We propose a rigorous approach wherein connectivity between co-activated regions is first modeled using exploratory Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.

scholarly journals Pain Detection and the Privacy of Subjective Experience

2007 ◽  
Vol 33 (2-3) ◽  
pp. 433-456 ◽  
Author(s):  
Adam J. Kolber
Keyword(s):  
Subjective Experience ◽  
Brain Regions ◽  
Physical Pain ◽  
Medical Evidence ◽  
Functional Magnetic Resonance ◽  
Positron Emission ◽  
Pain Symptoms ◽  
The Brain ◽  
Insight Into ◽  
Neuroimaging Techniques

A neurologist with abdominal pain goes to see a gastroenterologist for treatment. The gastroenterologist asks the neurologist where it hurts. The neurologist replies, “In my head, of course.” Indeed, while we can feel pain throughout much of our bodies, pain signals undergo most of their processing in the brain. Using neuroimaging techniques like functional magnetic resonance imaging (“fMRI”) and positron emission tomography (“PET”), researchers have more precisely identified brain regions that enable us to experience physical pain. Certain regions of the brain's cortex, for example, increase in activation when subjects are exposed to painful stimuli. Furthermore, the amount of activation increases with the intensity of the painful stimulus. These findings suggest that we may be able to gain insight into the amount of pain a particular person is experiencing by non-invasively imaging his brain.Such insight could be particularly valuable in the courtroom where we often have no definitive medical evidence to prove or disprove claims about the existence and extent of pain symptoms.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

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