Mindfulness training preserves sustained attention and resting state anticorrelation between default‐mode network and dorsolateral prefrontal cortex: A randomized controlled trial

Abstract Mindfulness training can enhance cognitive control, but the neural mechanisms underlying such enhancement in children are unknown. Here, we conducted a randomized controlled trial (RCT) with sixth graders (mean age 11.76 years) to examine the impact of 8 weeks of school‐based mindfulness training, relative to coding training as an active control, on sustained attention and associated resting‐state functional brain connectivity. At baseline, better performance on a sustained‐attention task correlated with greater anticorrelation between the default mode network (DMN) and right dorsolateral prefrontal cortex (DLPFC), a key node of the central executive network. Following the interventions, children in the mindfulness group preserved their sustained‐attention performance (i.e., fewer lapses of attention) and preserved DMN–DLPFC anticorrelation compared to children in the active control group, who exhibited declines in both sustained attention and DMN–DLPFC anticorrelation. Further, change in sustained‐attention performance correlated with change in DMN–DLPFC anticorrelation only within the mindfulness group. These findings provide the first causal link between mindfulness training and both sustained attention and associated neural plasticity. Administered as a part of sixth graders' school schedule, this RCT supports the beneficial effects of school‐based mindfulness training on cognitive control.

There is considerable evidence that mindfulness training enhances cognitive control in adults and children (Chiesa, Calati, & Serretti, 2011;Mak, Whittingham, Cunnington, & Boyd, 2018), but the neural mechanisms of such enhancement are unknown. Here we asked in a randomized controlled trial (RCT) whether grade-wide mindfulness training in sixth graders would enhance sustained attention and, for the first time, assessed the underlying brain plasticity associated with mindfulness-driven gains in sustained attention in children.
A critical component of cognitive control is sustained attention, which involves the ability to focus on external, task-relevant stimuli and responses, and to suppress task-irrelevant thoughts and feelings (i.e., lapses of attention or mind wandering). These dual processes correspond to brain activations in two neural networks: the central executive network (CEN) and the default mode network (DMN). The CEN, with core nodes located in bilateral dorsolateral prefrontal cortices (DLPFCs) and bilateral parietal cortices, typically exhibits increased activation during engagement in attention-demanding tasks (Denkova, Nomi, Uddin, & Jha, 2019;Greicius, Krasnow, Reiss, & Menon, 2003;Mason et al., 2007;Weissman, Roberts, Visscher, & Woldorff, 2006).
Thus, sustained attention requires a balance between the CEN and DMN systems, operationalized as both increased activation of the CEN and reduced activation of DMN. Lack of segregation in activations of these networks leads to failure to attend to a task: Lapses in attention followed reduced activations in attention-related brain regions, including the CEN, along with reduced deactivations of the DMN (Weissman et al., 2006). Such functional segregation can also be measured as a negative correlation (or anticorrelation) in functional connectivity patterns between core nodes of these two networks. Indeed, studies have found enhanced anticorrelation between the DMN and DLPFC during attention-demanding tasks (Denkova et al., 2019;Greicius et al., 2003;Piccoli et al., 2015). Further, stronger anticorrelations between the DMN and a network of task-activated regions, including DLPFC, were associated with more consistent performance on an attention-demanding task (Kelly et al., 2008). Together, a line of task-based evidence demonstrates the relationship between sustained attention and DMN-CEN anticorrelation.
Resting-state studies have also revealed both positive and negative correlations (or anticorrelations) between brain regions. The positive correlations are observed between brain regions that have often been identified as supporting orchestrated functions (e.g., bilateral frontal and parietal regions which constitute the CEN, all related to cognitive control; Fox et al., 2005;Fox & Raichle, 2007;Posner et al., 2014). Such patterns of correlations at rest indicate that these regions are integrated as a network, even in the absence of a task.
Negative correlations, or anticorrelations, at rest are interpreted as segregations between networks that may be functionally competitive, such as the DMN and CEN. Indeed, an anticorrelation between DMN and CEN is observed even in the absence of task performance during resting state (Fox et al., 2005;Fox & Raichle, 2007;Posner et al., 2014), reflecting the functional segregation between these two networks. In addition, stronger DMN-CEN anticorrelation at rest has been associated with better cognitive control in adults; stronger resting-state anticorrelation (i.e., more negatively correlated) between MPFC and DLPFC has been associated with greater working memory capacity (Hampson, Driesen, Roth, Gore, & Constable, 2010;Keller et al., 2015;Whitfield-Gabrieli et al., 2018). In elderly adults, a weakening of DMN-CEN anticorrelation over 4 years was associated with a decline in processing speed (Ng, Lo, Lim, Chee, & Zhou, 2016).
Here, we asked whether mindfulness training in children would enhance sustained attention and whether such an enhancement would be related to brain plasticity in the relations between the DMN and CEN. Behavioral enhancement and brain plasticity seemed plausible because mindfulness entails a continuous practice in cultivating attention to the present moment while continuously rejecting distractions. Using an RCT design, we were able to compare the effect of mindfulness training versus computer coding training (i.e., active control) on neurocognitive processes. The intervention was grade-wide (i.e., all sixth graders in the school participated) and included 99 children (mean age 11.76 years). Results reported here come from the subset of children whose families opted to participate in a neuroimaging visit at pre-and post-intervention, which was approximately one-third (34.3%) of all children enrolled in the full-scale RCT (all families were invited).
In the present study, we measured sustained attention by performance on the Sustained Attention to Response Task (SART) before and after the interventions. This task requires the participant to press a button when presented with any digit (0-9, Go trials), except for the rarely presented "3" that appears on only 5% of trials (No-Go trials). As the task lasts approximately 15 min, it requires sustained attention for a tedious task over a long period. Performance on the Go trials provides a measure of sustained attention, whereas performance on the No-Go trials provides a measure of response inhibition (Allan Cheyne, Solman, Carriere, & Smilek, 2009;Robertson, Manly, Andrade, Baddeley, & Yiend, 1997a;Smallwood, 2013). We measured resting-state functional connectivity (rsFC) before and after the intervention.
We tested three main hypotheses. First, we asked whether the initial ability to sustain attention on the SART was associated with rsFC anticorrelation between DMN and CEN networks. This would be the first study to probe the link between sustained attention and patterns of rsFC in children. Second, we asked whether mindfulness training would enhance sustained attention on the SART relative to coding training. Third, we asked whether mindfulness training, relative to coding training, would strengthen DMN-CEN anticorrelation.
Further, to directly associate behavioral and brain plasticity, we examined whether pre-post intervention changes in sustained attention and in DMN-CEN anticorrelation would be correlated among the children who received the mindfulness training. The RCT design of the study could provide novel causal evidence about the effect of mindfulness training on sustained attention and its underlying brain plasticity.

| Participants and randomization procedures
Ninety-nine sixth graders at the Boston Collegiate Charter School, a public charter school in Dorchester, MA, were randomly assigned to either a mindfulness training group or a coding training group during which they learned about computer coding. These interventions lasted for 8 weeks and took place during the last class period of their school-day schedule, which is typically reserved for miscellaneous school-related activities. All students were invited to participate in the brain imaging protocol at the Massachusetts Institute of Technology, of whom 40 students volunteered and completed the imaging protocol.
Participant characteristics were that 70% were female; 47.5% had ever been on the free/reduced price lunch (FRPL) program for low-income families; and 10% were Hispanic, 32.5% were African American, 52.5% were White, and 5% other or multiple racial identities (see Table 1).

Pre-intervention measures included the Wechsler Abbreviated
Scales of Intelligence for IQ [WASI, Wechsler, 1999] and the Edinburgh assessment of handedness (Oldfield, 1971) administered prior to randomization. For the randomization process, we stratified on indicator variables of whether a student participated in the imaging protocol and their handedness. We ran 1,000 randomizations and calculated the Mahalanobis distance between the mindfulness training and coding training group in order to create a single multivariate distance metric for the following student characteristics: sex, age, race and ethnicity, special education, FRPL, and prior performance on state standardized test scores (Morgan & Rubin, 2012). We selected the randomization combination that minimized the Mahalanobis Distance to further reduce omitted variable biases along with the RCT design; this approach has been increasingly used by other RCTs so as to equate randomized groups on multiple dimensions (Morgan & Rubin, 2012).
Forty children completed the baseline (pre-intervention) behavioral assessments and imaging protocol (Table 1). Thirty-one children were included (15 in the mindfulness training, 16 in the coding training) after removing participants due to scanning contraindications (i.e., getting braces), excessive movement, and missing data (see Section 2.11.2 and Each class incorporated 5-15 min of mindfulness exercises requiring participants to attend to some aspect of present-moment experience (e.g., sensations of breathing, sensations of the body, sounds in and out of the room, thoughts, or emotions) and to refocus attention on the present moment when the mind engages with cognitive processes (e.g., thinking about the past or future) or meta-cognitive processes (e.g., appraising thoughts). Participants shared their experiences with the class and received personalized feedback from the instructor. Class content was designed to provide a clear set of strategies for practicing mindfulness as well as foster a conceptual understanding of mindfulness practice. Classes focused on (a) sitting in an upright posture with backs straight and gaze lowered or eyes closed, (b) distinguishing between naturally arising thoughts and elaborated thinking, (c) minimizing the distracting quality of past and future concerns by reframing them as mental projections occurring in the present, (d) using the breath as an anchor for attention during mindfulness exercises, (e) repeatedly counting up to five consecutive exhalations, and (f) allowing the mind to rest naturally rather than trying to suppress the occurrence of thoughts.
The course lasted 8 weeks during which students met four times per week for 45-min classes, totaling approximately 24 hr of group practice and instruction by the end of the intervention. The three trained instructors who led the intervention each had practical knowledge and experience in mindful awareness, as well as teaching mindfulness to children.

| Coding training group
The SCRATCH (n.d.) computer programming curriculum was adapted to match the time commitment and novel engagement of the mindfulness intervention curriculum. The SCRATCH curriculum was designed to train skills of creative thinking, systematic reasoning, and collaborative work. The course met at the same time as the intervention group and also totaled approximately 24 hr of group practice and instruction by the end of the 8 weeks.
SCRATCH is a programming language and an online community where students program and share interactive media such as stories, games, and animations among them and with people from all over the world. Each class introduced step-by-step simple mathematical and computational ideas that were built into the SCRATCH experience in the first 15 min of the class. Students then applied the new knowledge to advance the creation of their individual programs in SCRATCH, thus applying core computational concepts such as iteration, conditionals, coordinates, variable random numbers, and so forth.
Students were encouraged to share their experiences and their creative thoughts with the class and received personalized feedback from the instructor. Participants were also encouraged to work collaboratively and reason systematically. The two trained instructors who led the intervention each had practical knowledge and experience with the SCRATCH curriculum, and working with children.

| Sustained Attention to Response Task
We measured attentional characteristics through the SART (Robertson, Manly, Andrade, Baddeley, & Yiend, 1997b; Figure 1). The SART is a Go/No-Go task with a high probability of "Go" signals. The SART paradigm was programmed using PsychoPy (Peirce, 2007), a python library for conducting psychological experiments. Participants were instructed to withhold responses (i.e., not pressing space bar) for the number 3 (target: "No-Go") and to respond quickly for all other numbers (nontargets: "Go").
Participants were instructed to respond both accurately and quickly. Participants could respond either during the stimulus display or during the intertrial interval (ITI). Participants performed a practice block consisting of 172 target and nontarget trials, immediately followed by the experimental session consisting of 2 series of 280 individual digits (28 of which were targets or 5%) for 250 ms each with an ITI of 900 ms between each digit. Trial order was pseudorandomized so that target trials were always separated by at least two nontarget trials. Participants had the option of an undefined break (not exceeding 5 min) before starting the second series. The task took approximately 15 min. to complete.

| Attentional performance variables
The primary outcomes of the SART were accuracy on "Go" trials (nontargets) and "No-Go" trials (targets). Accuracy on "Go" trials (hereafter: RTs by their mean RT for correct trials, with trials under 100 ms also removed. Greater ICV reflects a more variable response speed and has been implicated as a marker of off-task thinking (Bastian & Sackur, 2013).

| Student acceptability of interventions
We assessed student acceptability for both mindfulness and the coding training through post-intervention surveys (Bluth et al., 2016;Britton et al., 2014;Finucane & Mercer, 2006). Four 5-point Likertscale questions asked students to assess (a) their overall rating of the class, (b) the amount of work they had to do, (c) the degree of active participation, and (d) how much practical knowledge they learned.

| Procedures and blinding
The SART was administered immediately pre-and post-intervention.
In addition, student acceptability surveys were collected at the end of each intervention. At each time point, trained researchers met with students in their respective homeroom classes during the school day to complete the SART in one session. The SART was administered at students' original classes (before randomization) to ensure blinding of group assignment to testers. Students and teachers were instructed to not reveal group assignment. Pre-and post-intervention MRI protocols were collected before and after the end of the intervention for all participants. Pre-intervention MRI protocols were also administered before randomization to ensure blinding of group assignment to testers. MRI technicians and researchers were blind to group assignment at all times and participants were explicitly told not to reveal group assignment at any point.

| MRI acquisition
At both neuroimaging sessions, participants underwent a 6-min resting state scan where they were instructed to passively view a fixation cross during the scan period and not to close their eyes, sleep or engage in any mindfulness or other exercises for relaxation practices.
Specific instructions were "Keep your eyes open, relax, try not to move and try to stay awake." All scans were acquired using a 3 T Trio MR System with a 32-channel, phased-array head coil (Siemens Healthcare, Erlangen, Germany). Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired using a gradient-echo, echo-planar imaging pulse sequence (EPI) with prospective acquisition correction (PACE) for motion (Thesen, Heid, Mueller, & Schad, 2000) with imaging parameters: repetition time ( Participants viewed a continuous string of single digits and were instructed to press the spacebar to all digits except 3 ("Go" trials) while withholding response to any 3 ("No-Go") trials). The total time for the task was 15 min with two series and a total number of 560 individual digits (5% were targets) position of the field-of-view and slice alignment during acquisition.
The parameters for each time point are updated based on motion correction parameters calculated from the previous two time points.
Five dummy scans were included at the start of the sequence. Additional structural scans were acquired using a three-dimensional  Statistical significance level was set at .05.

| Effect of training on Go-Accuracy
Statistical tests for Go-Accuracy were conducted using R Studio version 1.0.136 with R version 3.6.0 (R-Project. R Core Team, 2014).
Regression analysis was used to assess the causal impact of mindfulness training. The model regressed Go-Accuracy outcomes on intervention group assignment (1 for mindfulness training, 0 for coding training) taking into account pre-intervention performance as covariate.
We use heteroscedasticity-consistent standard errors for all models.
Statistical significance level was set at .05.

| rs-fMRI data analysis experimental design
Primary neuroimaging analysis was restricted to the DMN and CEN as a priori networks of interest based on the frequent involvement of these networks in cognitive control. First, we examined the relation of DMN-CEN anticorrelation to baseline variation in SART Go-Accuracy.
Second, we examined how the anticorrelation changed as a consequence of mindfulness training versus coding training, and whether this change was related to changes in SART Go-Accuracy.

| Preprocessing
Data preprocessing was done using SPM12 (Friston, 2007), which for the resting state scans included motion correction, slice timing correction, normalization with respect to the EPI template (sampling size was matched to the native 2-mm isotropic resolution) provided by SPM, and 8-mm Gaussian smoothing. Structural scan was normalized with respect to SPM's T1 template. Finally, image segmentation was carried out on the T1-weighted images to yield gray matter, white matter (WM), and cerebrospinal fluid (CSF) masks in normalized space (Ashburner & Friston, 2005). Additional preprocessing steps were carried out using the CONN toolbox version 19.d (Whitfield-Gabrieli & Nieto-Castanon, 2012). This included denoising using a Compcor (anatomical component-based noise correction method) (Behzadi, Restom, Liau, & Liu, 2007)  and framewise outliers were included as nuisance covariates in our subsequent first-level analyses. After denoising, the residual BOLD time courses from the networks were extracted to obtain correlation maps. For first-level functional connectivity analysis, Pearson's correlation coefficients were generated by computing correlations between the DMN time series and time series of all other voxels in the brain.

| Functional connectivity analyses
These seed-to-voxel r maps were then transformed to z maps using Fisher's r-to-z transformation and brought up to a general linear model analysis at the second level for within-group and between-group comparisons. Finally, we performed an SVC (Poldrack, 2007;Worsley et al., 1996) on the a priori defined CEN mask.

| Pre-intervention relation of Go-Accuracy to DMN connectivity
To investigate the relationship between pre-intervention Go-Accuracy and DMN connectivity, we correlated the pre-intervention Go-Accuracy with the random effects connectivity maps at preintervention from the DMN network from all participants.

| Effect of training on DMN-CEN anticorrelation
Regression analysis was used to assess the causal impact of mindfulness training. The model regressed connectivity maps from the DMN network on intervention group assignment (1 for mindfulness training, 0 for coding training). Significant main effects and interactions were followed up with post hoc testing. All analyses controlled for pre-intervention performance, gender, and IQ to determine beta coefficients of the treatment effect. Unless otherwise stated all statistical analysis are nonparametric (1,000 permutations) with a height threshold of p < .05 at the voxel level and an extent threshold of FWE-corrected p < .05.
3.2 | Effect of mindfulness training program on SART performance

| Go-Accuracy
At baseline, Go-Accuracy was significantly less than 100%

| No-Go-Accuracy
There were no significant group differences in No-Go-Accuracy before

| Motion
There were seven participants who exceeded the 20% movement threshold of images (or less than 132 usable time points) with movement outliers (4 mindfulness) and were discarded from the analysis. were observed (median value mindfulness group at pre-intervention: 6.0, at post 6.0; coding group at pre-intervention: 6.2, at post 7.8).
These results show that there were no significant group differences in motion. There was also no correlation between Motion and Go-Accuracy (r = .1, p = .5). Given the importance of motion as a possible confound in functional connectivity, we performed analyses following Ciric et al. (2017) to evaluate the efficacy of our denoising strategy (see Figures  coding training: r = .002, p = .50; Figure 6).

| DISCUSSION
We discovered a neural network characteristic associated with variation in sustained attention in children, and through an RCT design found novel causal evidence that mindfulness training, relative to coding training, preserved sustained attention in association with preservation of that neural network characteristic. There were three major findings. First, prior to intervention, better sustained attention positively correlated with greater resting-state anticorrelation between two distinct brain networks across all children: the DMN (associated with mind-wandering and task-unrelated thoughts) and the right DLPFC and right parietal components of the CEN (associated with cognitive

| Greater sustained attention correlated with greater resting-state DMN-right DLPFC anticorrelation prior to intervention
The present findings provide initial evidence about how variation in sustained attention among children relates to variation in brain function.
Prior to intervention and across all children, greater sustained attention was associated with greater resting-state anticorrelation between the DMN and a major hub of the CEN, right DLPFC. This brain-behavior relation is consistent with prior findings in adults that the DMN and CEN play key roles in attentional processes and in individual differences in cognitive control. DMN-DLPFC anticorrelation is enhanced during attentiondemanding tasks (Denkova et al., 2019;Greicius et al., 2003;Piccoli et al., 2015), and across individual adults stronger resting-state DMN-DLPFC anticorrelation is associated with greater working memory capacity (Hampson et al., 2010;Keller et al., 2015;Whitfield-Gabrieli et al., 2018) and faster processing speed (Ng et al., 2016). Conversely, such DMN-DLPFC anticorrelation is reduced when cognitive control processes are clinically impaired in individuals with ADHD (Hoekzema et al., 2014;Mattfeld et al., 2014)
In the current study, behavioral performance followed a particular pattern, with the two groups performing similarly at pre-intervention.

| Mindfulness training preserved DMN-right DLPFC anticorrelation
Group-differences in the strength of resting-state DMN-right DLPFC anticorrelation paralleled the behavioral findings for sustained attention. The mindfulness group maintained pre-intervention levels of DMN-right DLPFC anticorrelation, whereas the coding group exhibited reduced anticorrelations. This link between mindfulness training and intervention-related changes in brain-behavior associations was further supported by a correlation between changes in SART performance and changes in DMN-right DLPFC correlation that occurred only in the mindfulness group. This finding, to our knowledge, is the first evidence of a causal relationship between changes in sustained attention and changes in DMN-right DLPFC anticorrelations, in any age group. Previous studies reported a causal link between greater DMN activations and poorer vigilance (Hinds et al., 2013), and showed a causal inhibitory regulation of the CEN on DMN activations and connectivity patterns (Chen et al., 2013).
Another study found increased DMN-right DLPFC anticorrelation in patients with schizophrenia following an intervention of cannabis consumption (Whitfield-Gabrieli et al., 2018). This study found that the anticorrelation correlated with working-memory performance after intervention, but changes in performance were not correlated with changes in functional connectivity. Several prior studies have reported changes in rsFC in adults following mindfulness training, but the absence of behavioral measures precluded relating those changes in rsFC to any cognitive functions (Creswell et al., 2016;Taren et al., 2015;Taren et al., 2017;Yang et al., 2016). Given that the correlation between DMN-CEN transitions from positive to negative across early adolescent brain development (Chai et al., 2014;Sherman et al., 2014), our findings raise the possibility that mindfulness promotes the maturation of the neural circuits associated with cognitive control.
It is unclear why the coding group exhibited a decline in the DMN-right DLPFC anticorrelation following intervention. It is unlikely that the coding training itself diminished the anticorrelation because students rated the demands and enjoyment of the two interventions very similarly. Also, a lack of correlation between changes in SART performance and changes in anticorrelation in the coding group suggests that there was not a systematic relation between coding training and either behavioral or brain changes. One possibility is that the greater level of stress in the school year not only diminished sustained attention, but also the brain network connectivity (i.e., DMN-right DLPFC anticorrelation) that supports sustained attention. In this case, mindfulness training may be seen as a protective factor against such neurocognitive effects of stress, as it is for behavioral effects of stress (Jha, Stanley, Kiyonaga, Wong, & Gelfand, 2010).

| Limitations and implications
Several limitations of this study can be noted. First, there were a modest number of participants. In turn, this motivated an a priori approach to generate specific hypotheses about the neural networks that may change following a mindfulness intervention, so as to allow for a conservative level of statistics. It is unknown, therefore, whether other neural networks would also display training-induced resting-state plasticity. In addition, we utilized a network approach whereby all four seeds of the same network were analyzed together, in order to minimize the number of comparisons to be conducted. Second, although the study had an RCT design, the findings are from the subset of families who were willing to participate in neuroimaging. This resulted in an imbalance (although nonsignificant) in gender ratios between the two groups. However, we mitigated the effect of this imbalance by adding gender as a covariate in all analyses. Any additional characteristics that may have distinguished these families were equivalent across the two training groups.
The study also has several strengths. First, it generalizes the benefits from mindfulness training beyond both active engagement in meditation and task-specific brain plasticity. The changes in behavior and brain function occurred in a nonmeditative state. These findings are in agreement with the notion that mindfulness training transfers its effects to daily experiences beyond meditation practice (Lutz, Brefczynski-Lewis, Johnstone, & Davidson, 2008;Lutz, Dunne, & Davidson, 2007). Further, the observed functional brain differences were not limited to a specific task or activity because rsFC is thought to reflect primarily long-term, tonic network properties of neural systems that are shaped by experience and development and that have broad consequences for behaviors (Chai et al., 2014;Sherman et al., 2014). A second strength of this study is the association between a behavioral measure of attention and a separate measure of brain function. The finding of an objective neural correlate of sustained attention which tracks the beneficial behavioral effects following mindfulness intervention supports the validity of the behavioral results at both group and individual differences levels of analysis.
Finally, given that mindfulness training appears to have conferred a protective effect on sustained attention and DMN-right DLPFC anticorrelation, this finding emphasizes the value of including a randomized control group that helped to establish a true baseline against which a treatment effect could be discerned.
The present study found that a grade-wide, school-based mindfulness program preserved cognitive performance, which has important implications for mental health and educational practices. This is further corroborated by a previously reported finding on the impact of this intervention on social-emotional outcomes of reduced stress and reduced negative affect (Bauer et al., 2019). Indeed, the interaction between cognitive control and social-emotional functions are important in adolescent development. Reduced cognitive control in emotional contexts in adolescence has been associated with risk-taking behaviors, mental disorders, mortality, and crime (Coleman, 2011;Paus, Keshavan, & Giedd, 2008;Rudolph et al., 2017), whereas greater cognitive control has been linked to academic and professional success (Caspi, Entner Wright, Moffitt, & Silva, 1998;Finn et al., 2014;Finn et al., 2017;Moffitt et al., 2011). Finally, this RCT occurred at an urban school serving many students from lower income (low socioeconomic status) families, which was also reflected in the subgroup of students who participated in the imaging study. Thus, mindfulness training may be especially helpful in supporting cognitive control in students who may experience higher rates of early-life adversity. The present findings point to the neural mechanisms of how mindfulness training may promote healthy development of cognitive control as well as enhance well-being and academic achievement in youth.