In this new mini review from Frontiers in Cognition, Lippelt, Hommel, and Colzato compare three meditation types (focused attention, open monitoring and loving kindness) in terms of their effects on attention, conflict monitoring, and creativity.

The three research areas the authors covered in this review (attentional control, performance monitoring, and creativity or thinking style) seem to imply the operation of extended neural networks, which might suggest that meditation operates on neural communication, perhaps by impacting neurotransmitter systems. They speculate:

Finally, it may be interesting to consider individual differences more systematically. If meditation really affects interactions between functional and neural networks, it makes sense to assume that the net effect of meditation of performance depends on the pre-experimental performance level of the individual—be it in terms of compensation (so that worse performers benefit more) or predisposition (so that some are more sensitive to meditation interventions).

Full Citation: 
Lippelt DP, Hommel B and Colzato LS. (2014, Sep 23). Focused attention, open monitoring and loving kindness meditation: effects on attention, conflict monitoring, and creativity – A review. Frontiers in Psychology: Cognition. 5:1083. doi: 10.3389/fpsyg.2014.01083

Focused attention, open monitoring and loving kindness meditation: effects on attention, conflict monitoring, and creativity – A review

Dominique P. Lippelt, Bernhard Hommel and Lorenza S. Colzato

• Cognitive Psychology Unit, Institute for Psychological Research and Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands

Meditation is becoming increasingly popular as a topic for scientific research and theories on meditation are becoming ever more specific. We distinguish between what is called focused Attention meditation, open Monitoring meditation, and loving kindness (or compassion) meditation. Research suggests that these meditations have differential, dissociable effects on a wide range of cognitive (control) processes, such as attentional selection, conflict monitoring, divergent, and convergent thinking. Although research on exactly how the various meditations operate on these processes is still missing, different kinds of meditations are associated with different neural structures and different patterns of electroencephalographic activity. In this review we discuss recent findings on meditation and suggest how the different meditations may affect cognitive processes, and we give suggestions for directions of future research.

Introduction

Even though numerous studies have shown meditation to have significant effects on various affective and cognitive processes, many still view meditation as a technique primarily intended for relaxation and stress reduction. While meditation does seem to reduce stress and to induce a relaxing state of mind, it can also have significant effects on how people perceive and process the world around them and alter the way they regulate attention and emotion. Lutz et al. (2008) proposed that the kind of effect meditation has is likely to differ according to the kind of meditation that is practiced. Currently the most researched types of meditation include focused attention meditation (FAM), open monitoring meditation (OMM), and loving-kindness meditation (LKM). Unfortunately, however, the methodological diversity across the available studies with regard to sample characteristics, tasks used, and experimental design (within vs. between group; with vs. without control condition) renders the comparison between them difficult. This review is primarily focused on FAM and OMM studies¹ and on how these two (proto-)types of meditation are associated with different neural underpinnings and differential effects on attentional control, conflict monitoring, and creativity.

Meditation Types

Usually, FAM is the starting point for any novice meditator (Lutz et al., 2008; Vago and Silbersweig, 2012). During FAM the practitioner is required to focus attention on a chosen object or event, such as breathing or a candle flame. To maintain this focus, the practitioner has to constantly monitor the concentration on the chosen event so to avoid mind wandering (Tops et al., 2014). Once practitioners become familiar with the FAM technique and can easily sustain their attentional focus on an object for a considerable amount of time, they often progress to OMM. During OMM the focus of the meditation becomes the monitoring of awareness itself (Lutz et al., 2008; Vago and Silbersweig, 2012). In contrast to FAM, there is no object or event in the internal or external environment that the meditator has to focus on. The aim is rather to stay in the monitoring state, remaining attentive to any experience that might arise, without selecting, judging, or focusing on any particular object. To start, however, the meditator will focus on a chosen object, as in FAM, but will subsequently gradually reduce this focus, while emphasizing the activity of monitoring of awareness.

Loving-kindness meditation incorporates elements of both FAM and OMM (Vago and Silbersweig, 2012). Meditators focus on developing love and compassion first for themselves and then gradually extend this love to ever more “unlikeable” others (e.g., from self to a friend, to someone one does not know, to all living beings one dislikes). Any negative associations that might arise are supposed to be replaced by positive ones such as pro-social or empathic concern.

Meditation Types, Attentional Scope, and Endogenous Attention

Whereas some meditation techniques require the practitioners to focus their attention on only a certain object or event, other techniques allow any internal or external experiences or sensations to enter awareness. Different meditation techniques might therefore bias the practitioner to either a narrow or broad spotlight of attention. This distinction is thought to be most evident with regard to FAM and OMM. FAM induces a narrow attentional focus due to the highly concentrative nature of the meditation, whereas OMM induces a broader attentional focus by allowing and acknowledging any experiences that might arise during meditation.

In a seminal study, Slagter et al. (2007) investigated the effects of 3 months of intensive Vipassana meditation (an OMM-like meditation) training on the allocation of attention over time as indexed by the “attentional-blink” (AB) deficit, thought to result from competition between two target stimuli (T1 and T2) for limited attentional resources. After the training, because of the acquisition of a broader attentional scope, participants showed a smaller AB deficit as an indication of being able to distribute their brain-resource allocation to both T1 and T2. The reduced AB size was accompanied by a smaller T1-elicited P3b, a brain-potential thought to index attentional resource allocation.

A more recent study comparing meditators (trained in mindfulness-based stress-reduction) to non-meditators found that meditators show evidence of more accurate and efficient visual attention (Hodgins and Adair, 2010). Meditators monitored events more accurately in a concentration task and showed less interference from invalid cues in a visual selective attention task. Furthermore, meditators showed improved flexible visual attention by identifying a greater number of alternative perspectives in multiple perspectives images. Another study compared OMM and FAM meditators on a sustained attention task (Valentine and Sweet, 1999): OMM meditators outperformed FAM meditators when the target stimulus was unexpected. This might indicate that the OMM meditators had a wider attentional scope, even though the two meditator groups did not differ in performance when the stimulus was expected.

Electrophysiological evidence for meditation-induced improvements in attention comes from a recent study in which Vipassana meditators performed an auditory oddball task before and after meditation (in one session) and random thinking (in another session; Delgado-Pastor et al., 2013). The meditation session was composed by three parts. First, an initial part of self-regulation of attention focused on sensations from air entering and leaving the body at the nostrils. Second, a central part of focusing attention on sensations from all parts of the body while maintaining the non-reactivity and acceptance attitude. Last, a final brief part aimed on generating feelings of compassion and unconditional love to all living beings. Meditators showed greater P3b amplitudes to target tones after meditation than either before meditation or after the no-meditation session, an effect that is thought to reflect enhanced attentional engagement during the task.

Support for the assumption that FAM induces a narrow attentional focus comes from several studies that show that FAM increases sustained attention (Carter et al., 2005; Brefczynski-Lewis et al., 2007). Neuroimaging evidence by Hasenkamp et al. (2012) suggests that FAM is associated with increased activity in the right dorsolateral prefrontal cortex (dlPFC), which has been associated with “the repetitive selection of relevant representations or recurrent direction of attention to those items” (D’Esposito, 2007, p. 765 ). Thus, in the context of meditation experience, dlPFC might be involved in repeatedly redirecting or sustaining attention to the object of focus. It would be interesting to investigate whether this pattern of activation is unique to FAM or whether other kinds of meditation lead to similar increases in activity in the dlPFC. If the dlPFC is indeed involved in the repetitive redirection of attention to the same object of focus, then it should not be as active during OMM during which attention is more flexible and continuously shifted to different objects. Alternatively, however, if during OMM the meditator achieves a state of awareness where (only) awareness itself is the object of focus, the dlPFC might again play a role in maintaining this focus. Similarly, it would be interesting to examine how LKM modulates attentional processes and the activation of the dlPFC.

In a follow-up study, Hasenkamp and Barsalou (2012) found that, during rest, the right dlPFC connectivity to the right insula was improved in experienced meditators compared to novices. The authors suggest that improved connectivity with the right insula might reflect enhanced interoceptive attention to internal bodily states. In a support of this idea, a recent study reports that mindfulness training predicted greater activity in posterior insula regions during interoceptive attention to respiratory sensation (Farb et al., 2013). Various studies have shown theta activity to be increased during meditation, primarily OMM-like meditations (e.g., Baijal and Srinivasan, 2010; Cahn et al., 2010; Tsai et al., 2013; for review see Travis and Shear, 2010). This increase in theta activity, usually mid-frontal, has been suggested to be involved in sustaining internalized attention. As such, similar increases in theta activity would be expected for LKM during which attention is also internalized, but not during FAM where attention is explicitly focused on an external object, even though typically the object of meditation in FAM, at least for beginners, is the breath, which is internal.

Additionally, active mindfulness meditation (versus rest) was associated with increased functional connectivity between the dorsal attention network, the Default Mode Network and the right prefrontal cortex (Froeliger et al., 2012). Thus, meditation practice seems to enhance connectivity within and between attentional networks and a number of broadly distributed other brain regions subserving attention, self-referential, and emotional processes.

Meditation Types and Conflict Monitoring

A fundamental skill acquired through meditation is the ability to monitor the attentional focus in order to “redirect it” in the case of conflicting thoughts or external events. Not surprisingly, several studies have already shown improvements in conflict monitoring after meditation. Tang et al. (2007) investigated whether a training technique based on meditational practices called integrative body-mind training (IBMT; most similar to OMM) could improve performance on an attentional network task (ANT; Fan et al., 2002). The ANT was developed to keep track of three different measures, namely orientation, alerting, and conflict resolution. While IBMT had no effect on orienting and alerting scores, it did improve conflict resolution. In a similar study FAM and OMM were compared on an emotional variant of the ANT. Both types of meditation improved conflict resolution compared to a relaxation control group (Ainsworth et al., 2013). Surprisingly, there was no difference between the two meditation types, even though, mindfulness disposition at baseline (i.e., trait mindfulness) was also associated with improved conflict resolution.

Further evidence for improvements in conflict monitoring come from a study investigating the effect of 6-week long FAM trainig (versus relaxation training and a waiting-list group) on a discrimination task intended to investigate the relationship between attentional load and emotional processing (Menezes et al., 2013). Participants had to respond to whether or not the orientation of two lines presented to either side of an emotionally distracting picture was the same. Importantly, those who underwent a meditation or relaxation training commited fewer errors than the waiting list control group. Furthermore, error rates were lowest in the meditation group, higest in the waiting list group, while the relaxation group scored in between. With regard to emotional regulation meditators showed less emotional interference than the other two groups when attentional load was low, and only meditators showed a relationship between the amount of weekly practice and reductions in emotional interference.

In a study of Xue et al. (2011), meditation-naïve participants were randomly assigned to either an 11 h IBMT course or a relaxation training. Compared to the relaxation training, the IBMT group showed higher network efficiency and degree of connectivity of the anterior cingulate cortex (ACC). As the ACC is involved in processes such as self-regulation, detecting interference and errors, and overcoming impasses (e.g., Botvinick et al., 2004), improvements in ACC functioning might well be the neural mechanism by which IBMT improves conflict resolution. In an interesting study of Hasenkamp et al. (2012), experienced meditators engaged in FAM inside an fMRI scanner and pushed a button whenever they started to mind-wander. The moment of awareness of mind-wandering was associated with increased activity in the dorsal ACC. Thus, as the mind starts to wander during meditation, the ACC might detect this “error” and feed it back to executive control networks (Botvinick et al., 1999; Carter and van Veen, 2007), so that attention can be refocused. Various other studies have also shown improvements in ACC functioning after meditation (Lazar et al., 2000; Baerentsen et al., 2001; Tang et al., 2009, 2010). Hölzel et al. (2007) compared experienced and novice meditators during a concentrative meditation (akin to FAM) and found that the experienced meditators showed greater activity in the rostral ACC during meditation than the novices, even though the two groups did not differ on an arithmetic control task. Similar results were obtained in another study comparing novices and experienced meditators (Baron Short et al., 2007) by showing more activity in the ACC during FAM compared to a control task. The activity in the ACC was more consistent and sustained for experienced meditators. Related to that, Buddhist monks exhibited more activity in the ACC during FAM than during OMM (Manna et al., 2010). This suggests that the effects of meditation on the ACC and conflict monitoring do not seem to be limited to temporary state effects but carry over into daily life as a more stable “trait.” Future large scale longitudinal studies should to be conducted to address this issue and to disentangle short-term and long-term effects on conflict monitoring.

Improved conflict monitoring does not necessarily entail increased brain activity. Kozasa et al. (2012) compared meditators and non-meditators on a Stroop task in which semantic associations of words have to be suppressed to retrieve the color of the word. While behavioral performance was not significantly different for the two groups, compared to meditators, the non-meditators showed more activity in brain regions related to attention and motor control during incongruent trials. Given that the aim of many meditation techniques is to monitor the automatic arise of distractible sensations, such skill may become effortless by repeated meditation, therefore leading to less brain activity during the Stroop task. LKM has been shown to improve conflict resolution, as well, when LKM and a control group were compared on a Stroop task. The LKM group was faster in responding to both congruent and incongruent trials, and the difference between congruent and incongruent trials (the congruency effect) was smaller as well (Hunsinger et al., 2013). As LKM incorporates elements of both FAM and OMM, it would be interesting to investigate how the effect size associated with LKM may be positioned in between FAM and OMM.

Recently, meditators and non-meditators were compared with regard to measures of cortical silent period and short intra cortical inhibition over the motor cortex before and after a 60 min long meditation (for the meditators) or cartoon (for the non-meditators), respectively, measuring GABA_B receptor-mediated inhibitory neurotransmission and GABA_A receptor-mediated inhibitory neurotransmission (Guglietti et al., 2013). Given that deficits related to cortical silent periods in the motor cortex had been previously associated with psychiatric illness and emotional deregulation, the activity over the motor cortex was measured. No differences were found between meditators and non-meditators before the meditation/cartoon. However, after meditation there was a significant increase in GABA_B activity in the meditator group. The authors suggest that “improved cortical inhibition of the motor cortex, through meditation, helps reduce perceptions of environmental threat and negative affect through top down modulation of excitatory neural activity” (Guglietti et al., 2013, p. 400). Future research might investigate whether similar GABA related mechanisms underlie the suppression of distracting stimuli during meditation and how different types of meditation might have distinguishable effects on these processes.

Meditation Types and Creativity

The scientific evidence regarding the connection between meditation and creativity is inconsistent. While some studies support a strong positive impact of meditation practice on creativity (Orme-Johnson and Granieri, 1977; Orme-Johnson et al., 1977), others found only a weak association or no effect at all (Cowger, 1974; Domino, 1977). Recently, Zabelina et al. (2011) found that a short-term effect of mindfulness manipulation (basically OMM) facilitated creative elaboration at high levels of neuroticism. As pointed out by Colzato et al. (2012), these inconsistencies might reflect a failure to distinguish between different and dissociable processes underlying creativity, such as convergent and divergent thinking (Guilford, 1950). Accordingly, Colzato et al. (2012) compared the impact of FAM and OMM on convergent thinking (a process of identifying one “correct” answer to a well-defined problem) and divergent thinking (a process aiming at generating many new ideas) in meditation practitioners. Indeed, the two types of meditation affected the two types of thinking in opposite ways: while convergent thinking tended to improve after FAM, divergent thinking was significantly enhanced after OMM. Colzato et al. (2012) suggest that FAM and OMM induce two different, to some degree opposite cognitive-control states that support state-compatible thinking styles, such as convergent and divergent thinking, respectively. In contrast to convergent thinking, divergent thinking benefits from a control state that promotes quick “jumps” from one thought to another by reducing the top-down control of cognitive processing—as achieved by OMM.

Conclusion

Research on meditation is still in its infancy but our understanding of the underlying functional and neural mechanisms is steadily increasing. However, a serious shortcoming in the current literature is the lack of studies that systematically distinguish between and compare different kinds of meditation on various cognitive, affective or executive control tasks—a criticism that applies to neuroscientific studies in particular. Further progress will require a better understanding of the functional aims of particular meditation techniques and their strategies to achieve them. It will also be important to more systematically assess short- and long-term effects of meditation, as well as the (not yet understood) impact of meditation experience (as present in practitioners but not novices). For instance, several approaches (like Buddhism) favor a particular sequence of acquiring meditation skills (from FAM to OMM) but evidence that this sequence actually matters is lacking. Moreover, the neural mechanisms underlying meditation effects are not well understood. It might be interesting that the three main research topics we have covered in the present review (attentional control, performance monitoring, and creativity or thinking style) imply the operation of extended neural networks, which might suggest that meditation operates on neural communication, perhaps by impacting neurotransmitter systems. Finally, it may be interesting to consider individual differences more systematically. If meditation really affects interactions between functional and neural networks, it makes sense to assume that the net effect of meditation of performance depends on the pre-experimental performance level of the individual—be it in terms of compensation (so that worse performers benefit more) or predisposition (so that some are more sensitive to meditation interventions).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

1. ^ It is important to note that even though this mini review is based on the theoretical framework of distinguishing FAM and OMM, another one includes the distinction between concentrative meditations, practices that regulate or control attention/awareness, and meditation practices which instead do not explicitly target attentional/effortful control (Chiesa and Malinowski, 2011; see also Chiesa, 2012 for a recent review on the difficulty of defining Mindfulness). Moreover, Travis and Shear (2010) have pointed out a third meditation category besides FAM and OMM: the automatic self-transcending which trascends FAM and OMM through the absence of both (a) focus and (b) individual control or effort.

References at the Frontiers site

Correlating Objective Third-Person Brain fMRI Measures with Subjective First-Person Identification of Specific Somatosensory Sensations

This is a seriously geeky paper that seeks to identify and correlate objective brain imaging with subjective experiences of focused attention. Here is the major finding:

These results provide evidence that the frontopolar prefrontal cortex has dissociable functions depending on specific cognitive demands; i.e. the dorsal portion of the frontopolar prefrontal cortex in conjunction with primary somatosensory cortex, temporopolar cortex, inferior parietal lobe, hippocampus, insula and amygdala are involved in the processing of spontaneous general subjective somatosensory experiences disclosed by focused and sustained attention.

Translation: They found that a specific area of the prefrontal cortex (the dorsal portion of the frontopolar prefrontal cortex) works with other higher level brain regions (primary somatosensory cortex, temporopolar cortex, and inferior parietal lobe) as well as elements of the limbic system (hippocampus, insula and amygdala) to process focused attention on subjective somatosensory experiences.

This might seem like a whole lot of who cares, but understanding these processes might lead us to better and more effective models of somatic therapy for trauma. In trauma survivors, the connections between the limbic system (emotional processing) and the prefrontal cortex (executive functions such as planning, understanding eventual outcomes to current actions, and so forth) is often compromised in some way. Mindfulness of bodily experiences can help repair this, but knowing which other brain regions are involved might help us refine our tools to make them more effective.

Full Citation: 
Bauer CCC, Barrios FA, Díaz J-L. (2014, Aug 28). Subjective Somatosensory Experiences Disclosed by Focused Attention: Cortical-Hippocampal-Insular and Amygdala Contributions. PLoS ONE; 9(8): e104721. doi:10.1371/journal.pone.0104721

Subjective Somatosensory Experiences Disclosed by Focused Attention: Cortical-Hippocampal-Insular and Amygdala Contributions

Clemens C.C. Bauer, Fernando A. Barrios, José-Luis Díaz

Abstract

In order to explore the neurobiological foundations of qualitative subjective experiences, the present study was designed to correlate objective third-person brain fMRI measures with subjective first-person identification and scaling of local, subtle, and specific somatosensory sensations, obtained directly after the imaging procedure. Thus, thirty-four volunteers were instructed to focus and sustain their attention to either provoked or spontaneous sensations of each thumb during the fMRI procedure. By means of a Likert scale applied immediately afterwards, the participants recalled and evaluated the intensity of their attention and identified specific somatosensory sensations (e.g. pulsation, vibration, heat). Using the subject's subjective scores as covariates to model both attention intensity and general somatosensory experiences regressors, the whole-brain random effect analyses revealed activations in the frontopolar prefrontal cortex (BA10), primary somatosensory cortex (BA1), premotor cortex (BA 6), precuneus (BA 7), temporopolar cortex (BA 38), inferior parietal lobe (BA 39), hippocampus, insula and amygdala. Furthermore, BA10 showed differential activity, with ventral BA10 correlating exclusively with attention (r(32) = 0.54, p = 0.0013) and dorsal BA10 correlating exclusively with somatosensory sensation (r(32) = 0.46, p = 0.007). All other reported brain areas showed significant positive correlations solely with subjective somatosensory experiences reports. These results provide evidence that the frontopolar prefrontal cortex has dissociable functions depending on specific cognitive demands; i.e. the dorsal portion of the frontopolar prefrontal cortex in conjunction with primary somatosensory cortex, temporopolar cortex, inferior parietal lobe, hippocampus, insula and amygdala are involved in the processing of spontaneous general subjective somatosensory experiences disclosed by focused and sustained attention.

Introduction

Before attempting to explain how and why neurophysiological processes relate to consciousness traits, it seems necessary to find consistent correlations between subjective phenomenological features and brain activity patterns [1]. For example, it is now possible to correlate introspective evaluations of sensory aspects of subjective experience with imaged local brain activations [2]. Such neurophenomenological program depends on the development of dynamic approaches to cerebral activity in conjunction to standardized and rigorous measurements of subjective experience obtained from first-person reports [3], [4]. A particular difficulty concerning the subjective character of conscious experience is the neural substrate of sensorial qualia features such as color, sound, scent, taste, touch, pain, and the like [5]. It has been suggested that the ventral prefrontal cortex is necessary, but not sufficient, for the generation of subjective experiences [6]–[8] and that there may be different areas involved depending on their specific character (e.g. auditory, tactile, emotional) [9], [10]. Other studies also report signal increases in frontopolar prefrontal cortex during different self-referential processing tasks [11]–[13] and the magnitude and time course of its activation predicts whether information is consciously perceived or slips away unnoticed [6].

Bilateral activations of temporopolar cortex were found during object encoding, tactile perception and self-related processing [14]–[16]. Furthermore, the phenomenal character of perceiving some objects as different from others is associated with right temporopolar activation [15].

The ability to voluntarily direct, concentrate, and sustain attention can bring into focus and enhance bottom-up qualitative processes of either a somatosenory/external or proprioceptive/internal nature [17]–[21]. A form of insight meditation requiring sustained awareness of subtle somatic sensations spontaneously arising from different body parts increases parieto-occipital gamma activity, a marker for enhanced sensory awareness [22]. Tactile attention also biases the processing of selected stimuli relevant features by amplifying somatosensory cortex responses [23]. Attention towards particular somatic stimuli, in turn, selectively enhances domain-specific cortical representations that probably are determinant for their conscious perception [21], [24].

Based on previous studies implicating several brain regions in the generation of subjective experiences [6]–[8], [14], [15] and the evidence that top-down attention control can be used to define particular sensory targets [21], [22], [24], we hypothesized that focusing attention on subtle pre-reflective somatosensory experiences would activate frontopolar prefrontal and temporopolar cortices, and, specifically, that the objectively measured brain activity within these regions would correlate with subjective sensory experience reports.

Materials and Methods

Subjects

All subjects gave written informed consent for the experimental procedure, and the protocol follows the principles expressed in the Declaration of Helsinki and was authorized by The Bioethics Committee of the Neurobiology Institute (Comité de Bioética del Instituto de Neurobiología, Universidad Nacional Autónoma de México). After standard exclusion criteria for functional magnetic resonance imaging (fMRI) were applied, 37 healthy volunteers participated in the study (16 female and 21 male, mean age 35.58 years, SD 7.97, 14 left handed and 23 right handed). Subjects were evaluated with digital versions of the Symptom Checklist 90 and Edinburgh Inventory to exclude psychological and/or psychopathological symptoms, and to evaluate handedness [25], [26]. All subjects gave informed consent for the experimental procedure, and the protocol had IRB approval.

Experimental design

Brain activation was examined during covert focused attention directed towards either the right or left thumb under two experimental conditions: (a) External-Stimulus Condition (manual caressing of either thumb with a 2-cm sponge brush at 1–2 Hz and stimulation aftereffect) and (b) Spontaneous-Sensation Condition in absence of any external stimulation (Figure 1). Resting periods without attention tasks separated both experimental conditions. Subjects were instructed to focus their attention on either thumb during the two experimental conditions and to abstain from moving it during the whole experiment. The instructions emphasized that, in the absence of touch stimuli, the subjects should focus their attention on the spontaneous sensations arising from either thumb rather than visualizing or imagining this body part. The protocol consisted of a block design paradigm alternating between focusing of attention towards the External-Stimulus of either thumb (60 sec blue block in Figure 1) or focusing of attention towards Spontaneous-Sensation of the same body part in the absence of external stimuli (60 sec yellow block in Figure 1). The length of the blocks was decided after a pilot study where the response showed that the subjects started to feel clear and distinct sensations ~20–40 sec after the instruction. Right and left thumbs were run in separate procedures and the order of the thumb was randomly counterbalanced (Left thumb first 52%). The External-Stimulus block was further divided into a 30 sec Touch-Stimulus Condition (shown as a dark-block in Figure 1) and a 30 sec Stimulation Aftereffect Condition (shown as a light-blue block in Figure 1). External-Stimulus and Spontaneous-Sensation conditions were separated by 30 sec resting intervals to ensure no overlapping brain activity. Each run lasted 540 sec and consisted of three epochs. One epoch was a 180 sec sequence of Rest, Touch-Stimulus, Stimulation Aftereffect, Resting, and Spontaneous-Sensation. While in the scanner, the subjects received a previously agreed one-word instruction (“attention” or “rest”) via MRI compatible audio equipment (NordicNeuroLab, Bergen, Norway) directing them to focus their attention on the target thumb, or to rest. Subjects had their eyes closed during the whole experiment.

Figure 1. Single run experimental paradigm for either thumb.
Touch-Stimulus (TS, in dark-blue), Stimulation-Aftereffect (SA, in light-blue) and Spontaneous-Sensation (SS, in yellow). Focusing attention (FA, in grey) was required during every condition. No attention task was required during resting periods between conditions (gaps). doi:10.1371/journal.pone.0104721.g001

Immediately after the scanning procedure all subjects were submitted to a Phenomenology Questionnaire to assess first-person Subjective Sensations experienced during the Spontaneous-Sensation Condition. The Phenomenology Questionnaire was designed to reflect the participant's subjective assessment of their experience through all the blocks. It consisted of a qualitative free description of the experienced sensations followed by a quantitative section where attention strength and intensity of specific sensory qualia experienced across all Spontaneous-Sensation blocks were assessed by means of a 1 to 5 Likert scale (see below and Table 1 in Results section for details).

Table 1. Phenomenology Questionnaire.
doi:10.1371/journal.pone.0104721.t001

During the scanning an examiner closely monitored the subject's thumb to ensure there was no motion. If there was any perceptible movement the run was discarded. Only six runs from 3 subjects (all right handed) were discarded due to involuntary thumb movement, and the results presented were obtained from the remaining 34 subjects.

Imaging protocol

fMRI imaging was performed on a 3.0T GE MR750 instrument (General Electric, Waukesha, WI) using a 32-channel head coil. Functional imaging included 35 axial slices, acquired using a T2*-weighted EPI sequence with TR/TE 3000/40 ms, a 64×64 matrix and 4-mm slice thickness, resulting in a 4×4×4 mm³ isometric voxel. High-resolution structural 3D-T1-weighted images were acquired for anatomical localization (resolution of 1×1×1 mm³, TR = 2.3 sec, TE = 3 ms) covering the whole brain. The images were acquired with an acceleration factor = 2.

Quantitative evaluation of the Phenomenology Questionnaire

Attention strength towards each thumb was assessed with a Likert scale ranging from weak attention (1) to strong attention (5). Subject's subjective sensations scores for attention strength were used as covariates to model the attention regressor. A pilot study performed where volunteers were instructed to focus their attention on either thumb and generate an unrestricted phenomenological description revealed that the most frequently used adjectives were: pulsation, vibration, enlargement, heat, cold, shrinkage, itching, stinging, and numbness. Thus, these were the adjectives used in the subjective sensations Likert scale assessment ranging from no sensation (1) to intense sensation (5). The mean subjective sensation for all these nine somatosensory sensations was used as the covariate to model the qualia regressor (see row in Table 1 in Results section for details).

Image processing and statistical analyses

Functional image datasets were processed and analyzed with FSL 4.1.5 (FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl) [27].

Preprocessing.

The skull and other non-brain areas were extracted from the anatomical and functional scans using the script brain extraction tool (BET) of FSL, motion correction using MCFLIRT [28], spatial smoothing using a Gaussian kernel of FWHM 6 mm, mean-based intensity normalization, and nonlinear highpass temporal filtering. Extracted brains of all participants were linearly registered into the brain-extracted MNI152template using a linear spatial transformation function.

First-level fMRI analysis.

Statistical analysis was performed with FMRI Expert Analysis Tool using FMRIB's Improved Linear Model (FEAT FILM) Version 5.98 with local autocorrelation correction contrasts with a significance threshold criterion of Z>2.3 with a cluster significance threshold of P<0.05 corrected for multiple comparisons [29] and using the canonical hemodynamic response function (HRF) convolved with a function longer in duration to model the entire blocks and its time derivative as basic functions. The model included the following regressors with their corresponding HRF and their temporal derivatives: Touch-Stimulus and Spontaneous-Sensation as well as stimulation-aftereffect per thumb, with motion parameters controlled for in the model. The Touch-Stimulus regressor was modeled to fit a transient response curve in accord with previous somatosensory habituation reports [30], [31] where somatosensory cortex activation peaked around 6 sec after the onset of the stimulation and then exponentially returned to baseline for the rest of the block. In this manner it was ensured that only the touch-related processes were identified and measured. The Spontaneous-Sensation regressor was modeled to fit the last 30 sec of the block, as this would have stronger correspondence to the subjective ratings (see Experimental design), and the first 30 sec were modeled as dummy condition and discarded. Although all four conditions were considered in the GLM, only the response obtained for the Spontaneous-Sensation Condition of the last thumb of each participant was assessed and correlated with the subject's Subjective Sensation scores obtained from the Phenomenology Questionnaire. The rationale is that, although two functional runs were conducted (one for each thumb), the Phenomenology Questionnaire was only conducted once at the end of the session. Due to the recency effect, responses to this Questionnaire are more applicable to the last thumb stimulated, so only data acquired from the last functional run were analyzed with the Questionnaire data.

Group-level Subjective-Sensation analysis.

To identify activations at the group-level related to attention strength and subjective-sensation for somatosensory experiences, a subjective-sensation analysis using FLAME (FMRIB's Local Analysis of Mixed Effects) was conducted using subject's subjective sensations scores as covariates to model both attention and somatosensory experience regressors (see the Quantitative evaluation of the phenomenology questionnaire and rows A and of Table 1). All group Z statistical images were thresholded at Z>2.3 (p<0 .05) to define contiguous voxel clusters. The FSL cluster correction for multiple comparisons (Gaussian-random field theory based) was set at p<0.05, whole brain correction (http://www.fmrib.ox.ac.uk/fsl) [29]. Because we did not find any frontal activation at this threshold as previously hypothesized (see Introduction), we additionally performed an exploratory whole-brain group-level analyses using an uncorrected p-value of p<0.001 with a minimum cluster size threshold (k) of 15 voxels [32], [33]. This statistical threshold is in line with the recommendations for such complex and subtle cognitive processes, as used in previous social and affective neuroscience studies [33]. Subsequently, except where indicated, and due to the documented importance of the frontopolar prefrontal cortex in the integration of multiple separate cognitive processes in the service of higher-order behavioral goals like self referential processes (i.e. mentalizing) and attention [11], we specifically explored this region using a small-volume-correction through a region of interest (ROI) approach. The frontopolar prefrontal ROIs were based on the peak activation of this exploratory whole-brain group-level analyses and the results reported in the meta-analysis in Gilbert et all 2006 that specifically relate to left frontopolar cortex activation either during attention [34]–[37] or during self referential processes (i.e. metalizing) [11]–[13]. The ROIs were defined by merging individually created ROIs of 5 voxel (10 mm) diameter spheres (~131 mm³) around each of the documented peak coordinates and our own results in order to obtain oblong ROI volumes for a) Attention of k = 725 voxels (1450 mm³) and b) Subjective Sensation of k = 500 voxels (1000 mm³) covering the left frontopolar prefrontal cortex associated with these processes (ROIs were constructed in the 2 mm MNI-152 template). The statistical significance for the ROI analysis were corrected for multiple comparisons using the false discovery rate (FDR) correction as implemented in FSL [38]. The FDR procedure ensures that on average no more than 5% of activated voxels for each contrast are expected to be false positives. The resulting peak voxel activation for either regressor was used to calculate the percent changes of BOLD signal in each subject using Featquery (part of FSL 4.1.5). These signal changes were then correlated with the subject's individual specific subjective attention strength or mean somoatosensory qualia scores of the Lickert scale using Spearman's rank correlation coefficient (see rows A and of Table 1). Results were projected onto the surface representation of the MNI-152 template with the Freesurfer suite (http://surfer.nmr.mgh.harvard.edu/) [39] for visualization purposes.

Results

Qualitative evaluation of the phenomenology questionnaire

Subject's answers for the Phenomenology Questionnaire during the Spontaneous-Sensation Condition are shown in Table 1. All subjects experienced and spontaneously expressed their subjective sensations.

Spontaneous-Sensation analysis

Sixty-eight runs (34 right thumb and 34 left thumb) from 34 subjects were included in the analysis. Figure 2 shows that, compared with the resting task-free condition (neither external touch-stimuli nor spontaneous sensations), focusing of attention to Spontaneous-Sensation showed a group activation where the peak MNI coordinates for the right thumb (Figure 2B) were found in the left primary somatosensory cortex (BA 3b: X = −58 mm, Y = 6 mm, Z = 14 mm), bilateral secondary somatosensory cortices (SII: 34, 2, 20 and −42, −2, 12), left premotor cortex (BA 6: −2, 6, 52), left parietal lobe (PL: −26, −48, 26), left Broca's area (BA 44: −48, 4, −2), anterior cingulate cortex (BA 32: −18, 14, 28) and right insula (BA 13: 38, 10, 2). Focusing attention on Spontaneous-Sensation of the left thumb (Figure 2A), showed activations in the left primary somatosensory cortex (BA 3a: −46, 4, 16), left premotor cortex (BA 6: −56, 10, 42), and left Broca's area (BA 44: −50, 6, 8). Coordinates of peak activation, cluster size and z-values for this and all subsequent contrasts are shown in Table 2.

Figure 2. Spontaneous-Sensation analysis: Overall activations associated with focusing of attention during the different phases of the experimental paradigm. A) Focusing attention on Spontaneous-Sensation of the left thumb. B) Focusing of attention on Spontaneous-Sensation of the right thumb. All statistical maps had a significance threshold of Z>2.3, with a cluster significance threshold of p<0.05 (corrected for multiple comparisons). Images are presented in radiological convention and mapped to the MNI-152 template. doi:10.1371/journal.pone.0104721.g002

Table 2. Peak voxel activation for all experiments. doi:10.1371/journal.pone.0104721.t002

The activations found during the Touch-Stimulus Condition and their relation to the activations during the Spontaneous-Sensation Condition are detailed in a separate communication [21]. It is relevant to mention here that the contralateral activation of the somatosensory cortex (BA 3a/b corresponding to the hand area) obtained during the Touch-Stimulus Condition was also observed during the Spontaneous-Sensation Condition. Additionally, a left parieto-frontal activation was detected in the first-level analysis in the right-handed subjects during the Spontaneous-Sensation Condition. This prompted us to include a sample of 14 left-handed individuals for a statistically suitable comparison, but no differences between right and left-handed subjects were found after analyzing right and left thumbs separately and between groups for details please refer to [21]. Thus, we considered both hand-dominance groups as statistically similar and the left parieto-frontal activation as a result of top-down attentional mechanisms for a discussion on this please see [21].

Subjective-Sensation analysis

Attention.

left frontopolar prefrontal cortex (ventral portion) (BA 10: −4, 66, −4; Z = 3.78, p<0.05, small-volume-FDR-corrected; red cluster in Figure 3A) was active for attention as covariate and the percentage BOLD signal change correlated positively with the subjects' subjective attention strength reports (r(32) = 0.54, p = 0.0013, Figure 3B.1) but not for subjects' subjective somatosensory experience reports (r(32) = −0.1, p = 0.563, Figure 3B.4).

Figure 3. Subjective-Sensation analysis: Significant activations and correlations for the covariates from the Phenomenology Questionnaire.
A) Attention as a covariate revealed left ventral frontopolar prefrontal cortex (BA10 in red); Subjective somatosensory experience mean as a covariate revealed (in green) left dorsal frontopolar prefrontal cortex (BA10 in green), right primary somatosensory cortex (BA2), right premotor cortex (BA 6), precuneus (BA 7), left temporopolar cortex (BA 38), right inferior parietal lober (BA 39), right hippocampus, right insula and right amygdala, and. B) Spearman's rank correlations of subjective sensation scores with % BOLD signal change of peak voxels for 1) Subjective Attention score vs. ventral BA10 L, 2) Subjective Somatosensory Experiences vs. ventral BA 10 L, 3) Subjective Attention score vs. dorsal BA10 L, 4) Subjective Somatosensory Experiences vs. dorsal BA 10 L, 5) Subjective Somatosensory Experiences vs. BA 2 R, 6) Subjective Somatosensory Experiences vs. BA 6 L, 7) Subjective Somatosensory Experiences vs.BA 7 R, 8) 6) Subjective Somatosensory Experiences vs. BA 38 L, 9) Subjective Somatosensory Experiences vs. BA 39 R, 10) Subjective Somatosensory Experiences vs. Hippocampus R, 11) Subjective Somatosensory Experiences vs. Insula R, 12) Subjective Somatosensory Experiences vs. Amygdala R. Coordinates shown are X, Y, Z in mm for the MNI152 template. Activations have a significance threshold of Z>2.3, with a cluster significance threshold of p<0.05 (corrected for multiple comparisons) except for *BA10 = small-volume-FDR-correction with p<0.05. All correlations were assessed with the Pearson product-moment correlation and assessed for outliers using Spearman's rank-order correlation. Dashed lines indicate 95% confidence intervals. doi:10.1371/journal.pone.0104721.g003

Subjective Somatosensory Experiences.

Activity in several regions covaried with subjective somatosensory experiences (Figure 3A, green clusters, all clusters corrected p<0.05). These regions include the left frontopolar prefrontal cortex (dorsal portion) (BA10: −20, 72, 8), right primary somatosensory cortex (BA 2: 28, −42), right premotor cortex (BA 6: 50, 0, 28), right precuneus (BA 7: 8, −64, 52), left temporopolar cortex (BA 38: −32, 2, −18), right inferior parietal lobe (BA 39: 46, −70, 32), right hippocampus (30, −26, −14), right insula (38, −8, 4), and right amygdala (26, −8, −18). Additionally, the percentage BOLD signal change correlated positively with the subjects' subjective somatosensory experience reports (i.e. BA10: r(32) = 0.46, p = 0.007, Figure 3B.2; BA 2: r(32) = 0.36, p = 0.039, Figure 3B.5; BA 6: r(32) = 0.38, p = 0.029, Figure 3B.6; Precuneus: r(32) = 0.37, p = 0.034, Figure 3B.7; BA 38: r(32) = 0.33, p = 0.57, ρ = 0.4, p = 0.21, Figure 3B.8; BA 39: r(32) = 0.4, p = 0.02, Figure 3B.9; Hippocampus: r(32) = 0.51, p = 0.002, Figure 3B.10; Insula: r(32) = 0.38, p = 0.028, Figure 3B.11; Amygdala: r(32) = 0.36, p = 0.039, Figure 3B.12). Additionally, ventral BA 10 (−20, 72, 8) did not correlate with subjects' subjective attention strength reports (r = −0.07, p = 0.719, Figure 3B.3). To check for outliers we ran a non-parametric correlation test for all the brain areas, i.e. Spearman's rank-order correlation, which only showed a significant change for BA 38 (see above and Figure 3B.8).

Discussion

After verifying in 34 healthy volunteers that sustained attention directed to the spontaneous sensations of either thumb in the absence of any external stimuli effectively activates brain somatosensory areas, the present results show that corresponding subjective somatosensory experiences correlate with left dorsal frontopolar prefrontal cortex, right primary somatosensory cortex, left temporopolar cortex, right inferior parietal lobe, right hippocampus, right insula and right amygdala activations. Therefore, the main hypothesis of this work was largely corroborated with the additional finding that the left frontopolar prefrontal cortex (BA 10) and the temporopolar cortex (BA 38), in conjunction with primary somatosensory (BA 2), cortex, premotor cortex (BA 6), precuneus (BA 7), inferior parietal lobe (BA 39), hippocampus, insula and amygdala are involved in general spontaneous subjective somatosensory experiences.

The results show that the frontopolar prefrontal cortex has functional subdivisions, updating previous theories [11]. In particular, we show that the dorsal part of the frontopolar prefrontal cortex is involved during subjective sensory experiences known as qualia [6]–[8] and that it is coupled with other brain areas during this process. Hence, contributing to narrow down the individual brain structures involved [9], [10], [40]. In particular, our results agree with Feinstein et al. [6] in terms that the magnitude and time course of activation within the frontopolar prefrontal cortex, medial prefrontal cortex, and the anterior cingulate predict whether information is consciously perceived or slips away unnoticed. Other studies also report signal increases in frontopolar prefrontal cortex during different self-referential processing tasks [11]–[13]. It has also been shown that synchronic frontal gamma patterns (around 40 Hz) emerge with the recognition of a 3D object from an auto-stereogram and this pattern occurred only when subjects were readily expecting the arrival of the concealed visual object (26).

We also found that the left temporopolar cortex (BA38), together with the frontopolar cortex, becomes active during both attention mechanisms and subjective experience. Since the temporopolar cortex is a convergence zone where information from sensory, association, and limbic systems is integrated [41], [42]; this activation may relate to the awareness and conscious processing of the affective component of somatosensory experiences. In agreement with this interpretation, Ramsøy et al. [14] found that object encoding evokes bilateral activations of temporopolar, perirhinal, parahippocampal cortices, hippocampus and amygdala, while D'Argembeau et al. [43] found that the temporopolar cortex along with dorsomedial prefrontal cortex, left anterior middle temporal gyrus, and right cerebellum is implicated in reflective tasks pertaining to self, another person, and social issues.

Besides frontopolar and temporopolar activation, in the present study other areas appeared to be involved in the retrieval and processing of somatosensory experiences, i.e., primary somatosensory cortex, premotor cortex, precuneus, inferior parietal lobe, hippocampus, insula and amygdala. The combined activity of these areas probably supports conscious perceptual and phenomenological awareness [44], [45]. Consequently, pimary somatosensory cortex activation suggests its causal involvement due to the nature of the attended somatosensory experiences [21], [45]. Parietal and premotor cortices have been implicated in multisensory integration, embodiment, localization and self-attribution of body parts [46]–[49] and insula activation has been implicated in the integration of interoceptive and exteroceptive signals to construct the mental self [49], [50] and amygdala activation has been found coupled to frontal brain regions when subjects involve in self-related processing [43] and is probably a key node involved in self-referential emotion processing [51]–[53]. Finally, autobiographical memory and past experiences relate to consciousness of one self, which requires hippocampal processing [54]–[57]. Even though the instructions in our study focus on actual somatosensory experiences, the activations detected in these brain areas suggests an underlying neurocognitive requirement of body-ownership and self-consciousness. Finally, the noteworthy finding that primary somatosensory cortex is activated in the absence of external stimulation by the focusing of attention on spontaneous sensory qualia verifies that selective attention controlled by top-down cognitive processes enhance bottom-up qualitative processes of somatosensory/external and proprioceptive/internal nature that normally do not elicit primary somatosensory cortex activity in absence of stimuli [17]–[20]. This spontaneously-elicited somatosensory activity is accompanied by phenomenological somatosensory qualitative experiences or qualia, some of the most characteristic and enigmatic subjective phenomena [58], but suitable to be correlated with objective measures of brain activity [59].

Within a broader perspective, the study of sensory qualia intending to match third-person fMRI brain imaging with standardized first-person somatosensory reports constitutes a particular neurophenomenological endeavor to study the neural correlates of qualitative subjective experience. In the light of the present results, the precise mechanism for the production or correspondence of subjective sensory experiences in the detected neural networks remains a challenging, but perhaps a more delimited research question.

Supporting Information

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Acknowledgments

We are grateful to Dr. Luis Concha for his relevant comments and to Dr. G. Andrew James for his thorough and insightful review of the paper and wonderful suggestions to improve it. Also to M.Sc. Leopoldo González-Santos, M.Sc. Juan J. Ortiz, Dr. Sarael Alcauter, and Dr. Erick Pasaye for technical support.

Author Contributions

Conceived and designed the experiments: CCCB FAB JLD. Performed the experiments: CCCB FAB. Analyzed the data: CCCB FAB. Contributed reagents/materials/analysis tools: FAB. Contributed to the writing of the manuscript: CCCB FAB JLD.

References are available at the PLoS ONE site.

It's interesting to see the emerging research on differences between various approached to meditation. We may soon reach a point when we can "prescribe" meditation form A for increasing cognitive function or meditation form B for increasing compassion and empathy.

This study makes progress in that general direction.

The researchers demonstrate that focused-attention meditation (FAM) can increase performance on tasks associated with attention, while loving-kindness meditation (LKM) does not confer the same increase in performance.

On the other hand, both the FAM and LKM meditation practices seem to alter the way the brain responds to emotional (affective) pictures. When viewing sad face, FAM practitioners activated the same brain regions that were active in the attention tasks. However, the LKM practitioners responded to sad face with brain regions associated with "differentiating emotional contagion from compassion/emotional regulation processes."

The results in this study support the premise of neuroplasticity that specific practices are associated with specific changes in brain function.

Meditation does influence emotion processing, regardless of whether the practice focuses on cognition (ānāpānasati) or emotion (mettā). Finally, the neural pathways underlying emotion processing associated with LKM are likely to be different from those associated with FAM.

The article was published in PLOS ONE and is freely available online at the link below.

Distinct Neural Activity Associated with Focused-Attention Meditation and Loving-Kindness Meditation

Tatia M. C. Lee^1,2,3,4*, Mei-Kei Leung^1,2, Wai-Kai Hou^1,2,4, Joey C. Y. Tang^1,5, Jing Yin^4,6, Kwok-Fai So^3,4,7, Chack-Fan Lee^4,6, Chetwyn C. H. Chan^4,8*

1 Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong, China, 2 Laboratory of Cognitive Affective Neuroscience, The University of Hong Kong, Hong Kong, China, 3 The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China, 4 Social Neuroscience Research Network, The University of Hong Kong, Hong Kong, China, 5 Number Laboratory, The University of Hong Kong, Hong Kong, China, 6 Centre of Buddhist Studies, The University of Hong Kong, Hong Kong, China, 7 Department of Anatomy, The University of Hong Kong, Hong Kong, China, 8 Applied Cognitive Neuroscience Laboratory, Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China

Abstract

This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice) separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing.

Full Citation:  

Lee TMC, Leung M-K, Hou W-K, Tang JCY, Yin J, et al. (2012) Distinct Neural Activity Associated with Focused-Attention Meditation and Loving-Kindness Meditation. PLoS ONE 7(8): e40054. doi:10.1371/journal.pone.0040054