Alterations in task-induced activity and resting-state fluctuations in visual and DMN areas revealed in long-term meditators☆
Introduction
In recent years there is an increasing interest in the phenomenon of slow spontaneous fluctuations, emerging in the absence of sensory stimulation or an explicit task (also termed “resting-state”), which appears in all networks of the cerebral cortex (Biswal et al., 1995, Golland et al., 2007, Nir et al., 2008). A number of studies have demonstrated that these fluctuations show correlation structures (also termed “functional connectivity”) that reflect the co-activation of regions comprising fundamental networks that are activated or suppressed during task performance (Cordes et al., 2000, Fox et al., 2006, Smith et al., 2009).
The study of spontaneous “resting state” fluctuations has dramatically expanded in recent years, leading to detailed characterizations of these fluctuations across thousands of individuals. However, the source of the resting state fluctuations, and particularly – what can be learnt from their patterning – remains unknown (e.g. Moutard, Dehaene, & Malach, 2015). The present study was based on a recently proposed hypothesis named “spontaneous trait reactivation” (STR) (Harmelech & Malach, 2013), arguing that the patterning of spontaneous fluctuations represents not only large-scale, task related networks, but also subtle individual differences that reflect individually-unique habitual network activations. More specifically, the STR hypothesis suggests that spontaneous fluctuations can be actively remodeled in a long-term manner by focused and intense cortical training. While such potential links have been amply discussed in the clinical literature, there is limited evidence for it in healthy individuals. If this hypothesis has some validity, one would expect that individuals whose life-style entails unique habitual brain activations should show correspondingly unique changes in their resting state patterns. For example, the STR hypothesis predicts that professional musicians, jugglers or professional translators, should have subtle and unique differences in their resting state patterns compared to more typical individuals. The long-term meditators provide a unique testing ground along two main dimensions: first, the number of practice hours is especially long and habitual. Second, the type of attentional control is unique in its distinction from a direct engagement with sensory or physical activity, typical of other kinds of expertise.
A number of studies appear to be compatible with the STR hypothesis in showing the correlation of spontaneous fluctuations during resting-state with unique clinical populations (Greicius et al., 2009, Hahamy et al., 2014, Hahamy et al., 2015a, Hahamy et al., 2015b). More recently this correlation was extended even to the domain of gene expression (Richiardi et al., 2015).
However, in contrast to the rapidly expanding research relating resting-state changes to clinical populations, data about such associations in healthy individuals with unique habits which can be considered ‘cortical training’ is more limited (albeit see, for example, Adelstein et al., 2011, Guidotti et al., 2015, Johnen et al., 2015, Rohr et al., 2015). In that respect, individuals that practice various forms of meditation provide a unique testing ground for the STR hypothesis. These individuals engage in a unique life style, typically involving 0.5–1 h daily of a specific and highly controlled mental task – meditation – that is not shared by the general population. Thus, a straight forward prediction of our STR hypothesis was that these individuals should manifest unique changes compared to controls in their resting state patterns of spontaneous activations. Such tasks have been amply demonstrated to significantly alter brain function and network connectivity (Fox et al., 2014, Tang et al., 2012). Specifically, previous research has demonstrated reduced blood oxygenated level dependent (BOLD) signal level in the Default Mode Network (DMN) (Raichle et al., 2001) — a system associated with intrinsically-oriented and self-related functions (Buckner et al., 2008, Golland et al., 2007, Preminger et al., 2011) during various kinds of meditation. (e.g Brewer et al., 2011, Pagnoni et al., 2008, Pagnoni, 2012, but see also opposite evidence, Hölzel et al., 2007, Xu et al., 2014).
In parallel, changes in functional-connectivity during spontaneous resting-state fluctuations have been reported to occur in long-term meditators. Reports of such resting-state changes in the DMN include both reduced (Taylor et al., 2013) and increased (Jang et al., 2011, Taylor et al., 2013) functional connectivity among various DMN nodes, as well as altered functional connectivity between DMN nodes and regions outside of the DMN including the cognitive control Network (Brewer et al., 2011), sensory regions (Kilpatrick et al., 2011), and the orbitofrontal cortex (Hasenkamp and Barsalou, 2012, Jang et al., 2011).
However, the STR prediction has not been addressed by these previous studies. Thus, it is not clear whether the observed changes in the resting-state fluctuations reflect similar changes in task-induced activations in mindfulness meditation (MM).
It is important to emphasize that our study was not aimed at establishing a causal relationship between the practices of meditation and changes in resting-state fluctuations, but merely to explore the possible correlation between these two phenomena. This is reflected in the cross-sectional, instead of a longitudinal, design. Thus, we keep open the possibilities that the resting-state fluctuations may either reflect a personality trait that, on the one hand, leads individuals to more readily engage in meditation, and on the other hand, is reflected in concurrent changes of task-activations. Alternatively, the changes in resting state fluctuations may be due to causal changes produced by meditative training effects. Of course these possibilities are not mutually exclusive, and the correlation between rest and task may reflect both training and disposition effects.
To examine these questions, we compared brain activations during a visual recognition-memory task (VRM) and during resting-state in long-term meditators and controls. The VRM task had been used extensively in our laboratory, proving as an effective manner of evoking robust visual brain activations in a number of previous studies conducted on a typical population of participants (e.g. Yellin, Berkovich-Ohana, & Malach, 2015). We report here results obtained, specifically, in long-term Mindfulness meditators (MM). Mindfulness meditation is a technique aimed at cultivating an intentional focus of attention on momentary experiences, practiced by continuously returning one's focus from mind-wandering to the object of meditation such as the breath, while cultivating a receptive attitude towards all arising experiences (Gunaratana & Gunaratana, 2011). Accumulating evidence suggests that MM practice increases attention and emotion-regulation, as well as alters self-awareness (Chiesa and Serretti, 2010, Hölzel et al., 2011, Tang et al., 2015).
Section snippets
BOLD responses in the DMN and visual cortex
Contrasting all four visual stimuli vs. rest across the different ROIs in the control group revealed the expected task-induced increase in BOLD signal in visual ROIs (as previously shown, e.g. Yellin et al., 2015), as well as a corresponding task-induced reduction (relative to the fixation baseline) in BOLD signal in the DMN nodes (Fig. 1A). As can be seen in the whole brain maps, the MM group showed milder (less negative) task-induced reductions in BOLD signal in the DMN compared to the
Changes in Task-induced activations revealed in long-term meditators
Our results of the VRM task revealed a significant interaction in which visual ROIs tended to show increased activations, while the DMN ROIs showed a concomitant reduction in MM practitioners (Figs. 1, and 2A). Below we discuss these concomitant effects separately, starting with the observed group differences across the visual ROIs.
It is important to emphasize that due to the relative nature of BOLD fMRI, our results describe only the relative task vs. baseline responses. Hence, the source of
Participants
Eighteen healthy mindfulness meditators (MM, age 42.3 ± 9.9 years, 6 female), and eighteen meditation-naïve control participants (age 42.5 ± 10.4 years, 5 female) underwent fMRI scanning; all were right handed by self-report and had no history of neurological disorders. The participants were case–control matched primarily for age, as well as sex (accept one case). They were also matched for primary language (English in two cases, otherwise Hebrew), race (all Caucasian), and education (all having
Acknowledgments
The study was funded by the Helen and Kimmel Award for innovative Research (7204760501), The EU (FP7 VERE) (7107110504), The EU — Human Brain Project (7116580206) and the ISF-ICORE grants to R. M. (7111000508), the Teva Pharmaceutical Industries LTD fellowship to A. B.-O., as well as Israeli Presidential Bursary for outstanding PhD students in brain research to A.H.
References (70)
- et al.
Mindfulness-induced changes in gamma band activity — implications for the default mode network, self-reference and attention
Clin. Neurophysiol.
(2012) - et al.
Modulation of spontaneous fMRI activity in human visual cortex by behavioral state
NeuroImage
(2009) - et al.
Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI
NeuroImage
(2006) - et al.
Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners
Neurosci. Biobehav. Rev.
(2014) - et al.
Large-amplitude, spatially correlated fluctuations in BOLD fMRI signals during extended rest and early sleep stages
Magn. Reson. Imaging
(2006) - et al.
Resting state functional connectivity reflects abnormal task-activated patterns in a developmental object agnosic
NeuroImage
(2013) - et al.
When the brain loses its self: prefrontal inactivation during sensorimotor processing
Neuron
(2006) - et al.
Brain areas active during visual perception of biological motion
Neuron
(2002) - et al.
Neurocognitive biases and the patterns of spontaneous correlations in the human cortex
Trends Cogn. Sci.
(2013) - et al.
Large-scale mirror-symmetry organization of human occipito-temporal object areas
Neuron
(2003)
Differential engagement of anterior cingulate and adjacent medial frontal cortex in adept meditators and non-meditators
Neurosci. Lett.
Increased default mode network connectivity associated with meditation
Neurosci. Lett.
Impact of mindfulness-based stress reduction training on intrinsic brain connectivity
NeuroImage
Spontaneous fluctuations and non-linear ignitions: two dynamic faces of cortical recurrent loops
Neuron
Functional network organization of the human brain
Neuron
Stimulus-free thoughts induce differential activation in the human default network
NeuroImage
Individual Differences in Common Factors of Emotional Traits and Executive Functions Predict Functional Connectivity of the Amygdala
NeuroImage
Divide and conquer: a defense of functional localizers
NeuroImage
Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal
NeuroImage
Neural correlates of establishing, maintaining, and switching brain states
Trends Cogn. Sci.
Correlations between pupillary and fMRI BOLD timecourses uncover antagonistic relationship between high and low order visual areas during rest and imagery
NeuroImage
Nondirective meditation activates default mode network and areas associated with memory retrieval and emotional processing
Front. Hum. Neurosci.
Personality is reflected in the brain's intrinsic functional architecture
PLoS One
Electrophysiologic characteristics of respiratory suspension periods occurring during the practice of the Transcendental Meditation Program
Psychosom. Med.
Default network reduced functional connectivity in meditators negatively correlates with meditation expertise
Studying the default mode and its Mindfulness-induced changes using EEG functional connectivity
Soc. Cogn. Affect. Neurosci.
Functional connectivity in the motor cortex of resting human brain using echo-planar mri
Magn. Reson. Med.
Neural correlates of attentional expertise in long-term meditation practitioners
Proc. Natl. Acad. Sci. U. S. A.
Meditation experience is associated with differences in default mode network activity and connectivity
Proc. Natl. Acad. Sci. U. S. A.
Visual sensitivity and mindfulness meditation
Percept. Mot. Skills
The brain's default network: anatomy, function, and relevance to disease. In A. Kingstone & B. Miller (Eds.), the year in cognitive neuroscience 2008
Ann. N. Y. Acad. Sci.
A systematic review of neurobiological and clinical features of mindfulness meditations
Psychol. Med.
Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data
Am. J. Neuroradiol.
Mapping functionally related regions of brain with functional connectivity MR imaging
Am. J. Neuroradiol.
Functional and developmental significance of amplitude variance asymmetry in the BOLD resting-state signal
Cereb. Cortex
Cited by (43)
Comparing Neural Correlates of Consciousness: From Psychedelics to Hypnosis and Meditation
2023, Biological Psychiatry: Cognitive Neuroscience and NeuroimagingInter-participant consistency of language-processing networks during abstract thoughts
2020, NeuroImageCitation Excerpt :ii) a classical language task (“verbal fluency”), and iii) a visual imagery task (“imagine navigation”). Finally, in the last scan (Fig. 1D), each participant performed with their eyes open a Visual Categories, which was used to define the DMN regions for our group of participants, as previously reported (Berkovich-Ohana et al., 2016). The details of the stimuli used in each task are provided in Table 2.
Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: Part 2: Default mode network -preliminary evidence
2020, Psychiatry ResearchCitation Excerpt :Importantly, it has been also demonstrated that meditation practice modulates brain network integration (van Lutterveld et al., 2017) leads to the decreased DMN activation (Brewer et al., 2011; Hasenkamp and Barsalou, 2012) and increased DMNCEN anticorrelations (Bauer et al., 2019; Josipovic et al., 2012). Furthermore, several studies have shown that meditators, compared with nonmeditator groups, are more likely to engage task-positive brain regions (and not DMN) that are involved in conflict monitoring, working memory, and cognitive control (Bauer et al., 2019; Berkovich-Ohana et al., 2016; Froeliger et al., 2012; Lavallee et al., 2011; Lutz et al., 2008; Tang et al., 2017). Therefore, meditation may be a suitable candidate for modulating DMN and DMNCEN anticorrelation.
Contemplative neuroscience, self-awareness, and education
2019, Progress in Brain ResearchCitation Excerpt :More specifically, various mindfulness-related techniques showed decreased blood oxygenated level dependent (BOLD) fMRI activation in several areas of the DMN during practice, including the precuneus (Ives-Deliperi et al., 2011; Tang and Posner, 2009), mPFC (Brewer et al., 2011; Farb et al., 2007; Ives-Deliperi et al., 2011), PCC (Brewer et al., 2011; Pagnoni, 2012; Tang and Posner, 2009), and lateral temporal cortex (Pagnoni et al., 2008). Even more interesting, it was shown that these effects can become permanent, thus a trait and not only a state effect, as similar BOLD fMRI reductions in DMN activity were shown during task or rest (Berkovich-Ohana et al., 2016a,b; Garrison et al., 2015), as well as reduced DMN functional-connectivity during spontaneous resting-state fluctuations in long-term meditators (Berkovich-Ohana et al., 2016b; Taylor et al., 2013). Others reported increased functional-connectivity among various DMN nodes (Jang et al., 2011; Taylor et al., 2013), as well as altered functional-connectivity between DMN nodes and other networks (Brewer et al., 2011; Hasenkamp and Barsalou, 2012; Jang et al., 2011; Kilpatrick et al., 2011).
Mindfulness-based therapy improves brain functional network reconfiguration efficiency
2023, Translational Psychiatry
- ☆
Classification: Biological Sciences — Neuroscience