An Exploratory Functional Near-infrared Spectroscopy Study of Prefrontal Cortex Connectivity during Body Scan Meditation

Abstract

Objective

Body scan meditation is a popular mindfulness practice in which a person directs their attention toward internal bodily sensations. Although its neural mechanisms have been investigated using functional magnetic resonance imaging, few studies have used functional near-infrared spectroscopy (fNIRS) to directly measure prefrontal networks during body scan meditation.

Methods

In this study, symptoms of depression and anxiety were measured in 40 healthy young adults without prior meditation experience. Participants’ prefrontal networks were evaluated using fNIRS during body scan meditation and resting with nature sounds.

Results

Analyses of fNIRS data revealed significant positive prefrontal network connectivity in both conditions, with greater connectivity between the dorsolateral prefrontal cortex and medial prefrontal cortex observed when participants were resting with nature sounds than during body scan meditation. Correlation analyses showed that the left dorsolateral superior frontal gyrus–right medial superior frontal gyrus connectivity during body scan meditation was negatively associated with depressive and anxiety symptoms and positively associated with emotion regulation abilities.

Conclusion

Enhanced prefrontal networks induced by meditation may have therapeutic implications for mental health. The fNIRS findings, which measured direct changes in prefrontal networks during body scan meditation, could serve as a cornerstone for understanding the neural correlates.

INTRODUCTION

Body scan meditation is a popular form of focused attention meditation in which an individual directs their attention toward internal bodily sensations. It was first introduced as part of mindfulness-based stress reduction in clinical practice [1] and is now widely used in mindfulness-based interventions, although the specific effects of body scan meditation remain unclear [2].

Mindfulness-based interventions can alter activity in various brain regions, such as the insular region, which is related to self-awareness and interoception; the caudate, which is related to reward processing; and the prefrontal cortex, including the dorsolateral prefrontal cortex (DLPFC), which is related to attentional processing [3]. Focused attention directed toward one’s breath and body sensations during meditation enhances prefrontal cortex activation, particularly in the DLPFC and medial prefrontal cortex (mPFC) [4]. Furthermore, experienced meditators have shown increased functional connectivity between the attentional control and medial frontal regions during resting-state functional magnetic resonance imaging (fMRI), using seed regions identified from a prior focused-attention meditation study [5]. Although body scan meditation has characteristics of both focused attention and open monitoring meditation, it involves sequential attention to body sensations, making it closer to focused attention meditation [6]. Therefore, alterations in the connectivity between the medial and lateral prefrontal regions are expected during body scan meditation.

Although the neural correlates of focused attention meditation have been investigated using fMRI [4], studies that directly measure body scan meditation are scarce. In this study, we used functional near-infrared spectroscopy (fNIRS) to investigate functional connectivity among prefrontal brain regions during body scan meditation. The silent and noninvasive nature of fNIRS allows individuals to meditate comfortably without auditory disturbances or physical constraints, thereby facilitating conditions particularly favorable for body scan meditation, such as natural breathing patterns and posture [7]. The aim of our study was to use fNIRS to investigate changes in prefrontal connectivity during body scan meditation, with a particular focus on the connectivity between the DLPFC and mPFC.

METHODS

Participants

Fifty healthy young adults with no meditation experience were recruited through notices posted to the online bulletin boards of public or college Internet communities in Daegu, Korea. The exclusion criteria were acute or chronic psychiatric or physical illness, head trauma, or a history of addiction. Of the initial 50 participants, 3 with physical or psychiatric illnesses were screened out, and 7 more were excluded due to sleeping (1 person) or poor sensor contact (6 people) during the fNIRS experiment. Participants who completed the experiment received approximately US$40 (50,000 Korean won). The Ethics Committee of the Kyungpook National University Chilgok Hospital approved this study (IRB No. KNUCH2024-02-004).

Psychological Measurements

Nine questions from the Patient Health Questionnaire-9 (PHQ-9) [8] and seven from the Generalized Anxiety Disorder-7 (GAD-7) [9] were used to assess participants’ depressive and anxiety symptoms, respectively. Six items from the Korean Resilience Quesionnaires-53 (KRQ-53) [10] were used to evaluate emotion regulation ability.

Experimental Paradigm of the fNIRS

Hemodynamic responses were collected during the experiments using a portable fNIRS device (NIRSIT LITE; OBELAB, Inc.). The device includes five dual-wavelength laser diodes (780 and 850 nm) and seven photodetectors, providing 15 channels. It primarily captures signals from the prefrontal cortex (Fig. 1A). The DLPFC approximately corresponds to Brodmann areas 9 and 46, encompassing the middle frontal gyrus (MFG) and lateral portions of the superior frontal gyrus (SFG) [11]. Channels positioned over the MFG (channels 2−4 and 12−14) were designated as the DLPFC regions. Within the SFG, channels located over the dorsolateral subregion (dlSFG; channels 5 and 11) were included as components of the DLPFC, whereas channels over the medial subregion (mSFG; channels 6−10) were classified as components of the mPFC, consistent with previous subregional analyses of the SFG [12]. A clinical psychologist briefly introduced the fNIRS experiment to the participants and explained how to perform body scan meditation.

Fig. 1.

(A) Channel information. It is reproduced from NIRSIT Channel Information (https://www.obelab.com/info/notice.php), supplied by OBELAB Inc., with permission. The left dorsolateral superior frontal gyrus includes channel 11, and the medial superior frontal gyrus includes channels 6−10. (B) Correlation matrix representing functional connectivity among whole channels. (C) Differences in functional connectivity in the left dorsolateral superior frontal gyrus and medial superior frontal gyrus between conditions (resting with nature sounds > body scan meditation).

*p < 0.05, **p < 0.01.

After calibration and a 3-minute resting period, the two conditions were presented sequentially. A 5-minute script was developed to guide the participants in body scan meditation. Participants were instructed to sequentially direct their attention to internal sensations from head to toe. In the control condition, participants were asked to rest for 5 minutes while listening to nature sounds, including birdsongs. Resting with nature sounds was selected as the control condition because it induces relaxation and promotes an outward-directed attentional state [13], making it a more suitable comparator than resting in silence. A 2-minute break was provided between both conditions, the order of which was counterbalanced among participants.

Preprocessing and Analysis of the fNIRS Data

All preprocessing steps and connectivity analyses were performed using the NIRSIT Lite Analysis Tool program. Hemodynamic changes in each channel were calculated using the Modified Beer-Lambert Law. All data were applied to the band-pass filter from 0.005 to 0.1 Hz, and signal-to-noise ratio (SNR) outliers were checked for data quality. After the SNR values were thresholded by 30 dB, the SNR below the threshold was averaged and evaluated as an outlier by a cutoff z-score of 2.58 across all channels. The preprocessed oxygenated hemoglobin data were used to calculate connectivity among all channels based on Pearson correlations. The Statistical Package for Social Sciences 25 (https://www.ibm.com/kr-ko/products/spss-statistics) was used to perform correlation analyses between connectivity values and psychological measurements, controlling for age.

RESULTS

Demographic and Psychological Characteristics

The mean (± standard deviation) age of the 40 participants (16 men and 24 women) was 29.1 (± 6.0) years. The mean PHQ-9 and GAD-7 scores were 3.4 (± 2.9) and 2.4 (± 2.4), respectively. The mean emotion regulation subscale score for the KRQ-53 was 20.5 (± 4.5).

Prefrontal Networks during Body Scan Meditation and Resting with Nature Sounds

Prefrontal functional connectivity for body scan meditation and resting with nature sounds showed positive coefficients overall. All connectivity results are presented in Figure 1B. The mean of all pairwise correlations between signals from the DLPFC—including the dlSFG and MFG (channels 2−5 and 11−14)—and signals from the mPFC (channels 6−10) showed positive correlations (r = 0.632 for body scan meditation and r = 0.652 for resting with nature sounds, respectively).

Differences in DLPFC–mPFC Connectivity between Body Scan Meditation and Resting with Nature Sounds

The two conditions showed significant differences in DLPFC–mPFC functional connectivity. During body scan meditation, connectivity values were significantly lower than during rest with nature sounds between the dlSFG (channel 11) and mSFG (channel 7: p = 0.011; channel 8: p = 0.009; channel 10: p = 0.019; Fig. 1C).

Relationship between DLPFC–mPFC Connectivity and Psychological Measurements

In the body scan meditation condition, significant correlations were observed between the left dlSFG (channel 11)–right mSFG (channel 7) connectivity and PHQ-9 (r = −0.363, p = 0.023), GAD-7 (r = −0.382, p = 0.016), and emotion regulation (r = 0.344, p = 0.032) scores. Table 1 presents the other results.

Table 1.

Results of partial correlation analyses between prefrontal connectivity during body scan meditation and clinical measures

Variables\channelsa		11-7	11-8	11-10
1. PHQ-9	r	−0.363	0.107	−0.226
	p	0.023	0.517	0.167
2. GAD-7	r	−0.382	0.033	−0.218
3. Emotion regulation (KRQ-53)	r	0.344	0.079	0.091
	p	0.032	0.632	0.580

PHQ-9, Patient Health Questionnaire-9; GAD-7, Generalized Anxiety Disorder-7; KRQ-53, Korean Resilience Quetionnaires-53.

^aBased on templates provided by OBELAB, the dorsolateral superior frontal gyrus includes channel 11, and the medial superior frontal gyrus includes channels 7, 8, and 10.

DISCUSSION

Overall, the functional connectivity analyses revealed positive correlations within the prefrontal network, including DLPFC–mPFC connectivity, in both conditions. The body scan meditation involved focusing on internal sensations, whereas listening to nature sounds required engagement with external input. As noted in the Introduction, the DLPFC is a crucial region for cognitive control, such as resisting interference and maintaining attention [14]. The DLPFC plays a key role in sustaining attention toward stimuli and minimizing mind-wandering. Our participants demonstrated stronger prefrontal connectivity, particularly that involving the DLPFC, when resting with nature sounds than during body scan meditation. Notably, the participants reported no prior meditation experience and received only a brief explanation before practicing body scan meditation. Thus, they were likely more accustomed to focusing on nature sounds than bodily sensations. However, we posit that the results might differ for experienced meditators. Substantial evidence supports the role of meditation in enhancing DLPFC function. Meditation has been shown to augment both the function and connectivity of the DLPFC, including its connection to the right MFG [15]. A recent meta-analytic review found that focused attention meditation consistently activates regions, such as the DLPFC, anterior/posterior cingulate cortex, and insula, with experienced meditators showing higher activation in these networks [4]. Furthermore, Buddhist meditation has been associated with increased DLPFC activity [16]. Thus, our results could vary across participant groups, particularly among those with meditation experience.

Our correlation results revealed a relationship between left dlSFG (channel 11)–right mSFG (channel 7) connectivity and depression, anxiety, and emotion regulation scores during body scan meditation. The DLPFC is an important brain region implicated in psychopathology, including depression. The left DLPFC is a target region for the treatment of depression using transcranial magnetic stimulation, which induces changes in connectivity with the cingulate cortex [17]. Decreased DLPFC–dorsomedial prefrontal cortex connectivity may indicate treatment resistance, whereas increased DLPFC–anteromedial prefrontal connectivity may indicate recovery from depression [18]. In fNIRS studies, a negative correlation was observed between right DLPFC activity during the verbal fluency task and depressive symptoms [19,20]. Therefore, meditation-induced enhancement of prefrontal connectivity involving the DLPFC may be beneficial for psychological health, and our findings support this perspective.

This study had several limitations. First, our participants were healthy young adults with no prior meditation experience, making it difficult to generalize our findings to other groups. Second, our investigation focused exclusively on the prefrontal regions of the brain owing to the limited spatial coverage of the fNIRS device, despite its advantages such as providing a quiet noninvasive measurement environment. Third, the participants engaged in body scan meditation for a relatively short period. A 5-minute script was selected because the participants had no prior meditation experience. Although brief meditation sessions can positively affect mental health [21], the short duration used in this study may limit the generalizability of our findings to longer or more varied body scan meditation protocols. Although these limitations may have influenced our results, real-time fNIRS studies analyzing alterations in prefrontal networks during body scan meditation are scarce. We hope that our findings will serve as a cornerstone for understanding the neural correlates of body scan meditation.