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. Author manuscript; available in PMC: 2024 Jun 1.
Published in final edited form as: Suicide Life Threat Behav. 2023 Mar 21;53(3):510–521. doi: 10.1111/sltb.12960

Negative association between non-suicidal self-injury in adolescents and default mode network activation during the distraction blocks of a rumination task

Mindy Westlund Schreiner 1, Henrietta Roberts 2, Alina K Dillahunt 1,3, Brian Farstead 1, Daniel Feldman 1, Leah Thomas 1, Rachel H Jacobs 4, Katie L Bessette 1,5,6, Robert C Welsh 6, Edward R Watkins 2, Scott A Langenecker 1, Sheila E Crowell 1,7,8
PMCID: PMC10441767  NIHMSID: NIHMS1922494  PMID: 36942887

Abstract

Introduction:

Rumination, or repetitive and habitual negative thinking, is associated with psychopathology and related behaviors in adolescents, including non-suicidal self-injury (NSSI). Despite the link between self-reported rumination and NSSI, there is limited understanding of how rumination is represented at the neurobiological level among youth with NSSI.

Method:

We collected neuroimaging and rumination data from 39 adolescents with current or past NSSI and remitted major depression. Participants completed a rumination induction fMRI task, consisting of both rumination and distraction blocks. We examined brain activation associated with total lifetime NSSI in the context of the rumination versus distraction contrast.

Results:

Lifetime NSSI was associated with a greater discrepancy in activation during rumination relative to distraction conditions in clusters including the precuneus, posterior cingulate, superior, and middle frontal gyrus, and cerebellum.

Conclusion:

Difficulties associated with rumination in adolescents with NSSI may be related to requiring greater cognitive effort to distract from ruminative content in addition to increased attention in the context of ruminative content. Increasing knowledge of neurobiological circuits and nodes associated with rumination and their relationship with NSSI may enable us to better tailor interventions that can facilitate lasting well-being and neurobiological change.

Keywords: adolescents, default mode, distraction, neuroimaging, non-suicidal self-injury, rumination

INTRODUCTION

Non-suicidal self-injury (NSSI) includes purposeful self-harm without suicidal intent. Non-suicidal self-injury and suicidal behaviors are closely related, as NSSI is highly predictive of suicidal behavior and is associated with more lethal methods, multiple attempts, and hospitalizations relative to individuals without NSSI (Andover & Gibb, 2010; Horwitz et al., 2014; Mars et al., 2019). NSSI commonly begins in early-mid adolescence, a sensitive neurodevelopmental period for the emergence of psychopathology (Larsen & Luna, 2018; Lupien et al., 2009; Sydnor et al., 2021). Despite this vulnerability, neurodevelopmental plasticity during adolescence may offer a critical window in which the brain may be more amenable to prevention and intervention efforts because this is a time of rapid changes in synaptic pruning and myelination (Giedd, 2004; Giedd et al., 1999, 2009; Lenroot & Giedd, 2006; Paus et al., 2008). However, there is presently a dearth of interventions tailored to adolescents with NSSI and even fewer that are easily accessible. Thus, increasing our understanding of the neurobiological factors that contribute to NSSI may allow us to identify existing interventions, or develop novel interventions, to improve treatment outcomes.

Neurobiological research on the emergence of NSSI during adolescence, particularly using neuroimaging, has largely focused on brain regions implicated in affective processing. NSSI has been associated with amygdala connectivity at rest and during a facial emotion-processing task (Westlund Schreiner et al., 2017). Adolescents with NSSI also show greater anterior cingulate cortex (ACC) activation in response to emotional pictures, as well as greater amygdala and hippocampal activation relative to healthy controls (Plener et al., 2012). During social exclusion, youth with NSSI show elevated rostral ACC and ventrolateral prefrontal cortex activation, relative to depressed, non-NSSI youth and those with neither depression nor NSSI (Groschwitz et al., 2016).

While the extant literature on the neurobiology of NSSI is limited, studies investigating suicidality may also provide guidance regarding potential areas of interest for future research given the close relationship between suicidality and NSSI. For example, higher suicidality among adolescents with major depressive disorder (MDD) was (1) negatively associated with resting-state connectivity between the posterior cingulate cortex (PCC) and cerebellum, lateral occipital cortex, and temporal-occipital fusiform gyrus and (2) positively associated with connectivity between the precuneus and primary motor and somatosensory cortices and middle and superior frontal gyri (Westlund Schreiner et al., 2019; Zhang et al., 2016). In the context of task fMRI, studies have demonstrated that adolescents with MDD and suicide attempts show greater ACC activation in response to angry faces and lower activation of the primary sensory cortex in response to happy faces relative to healthy controls (Pan et al., 2013). These previous studies provide an important foundation for further inquiry. This could include research that uses larger sample sizes or that examines NSSI in a way that allows for clarification of which mechanisms may be attributed to depressive symptoms versus which mechanisms are more specific to NSSI.

While neurobiological research efforts to date have been largely focused on the role of emotion dysregulation and negative affect more generally in the context of self-injurious behaviors including NSSI (e.g., Franklin et al., 2012; Johnston et al., 2017; Kang et al., 2017; Klimes-Dougan et al., 2019), surprisingly little neurobiological research has investigated the association between NSSI and maladaptive thoughts and habits, such as depressive rumination. Rumination is common among individuals with NSSI and suicidal self-injury (Nagy et al., 2023; Rogers & Joiner, 2017; Watkins & Roberts, 2020) and is characterized by a habit of persistent and repetitive negative thinking. This repetitive negative thinking increases distress while serving as an impediment for active and adaptive problem-solving (Nolen-Hoeksema et al., 2008). Rumination often precedes, co-occurs with, and is predictive of, NSSI (Burke et al., 2016; Miranda & Nolen-Hoeksema, 2007; Nicolai et al., 2016; Selby et al., 2013, 2016; Valderrama et al., 2016). Importantly, persistent ideation about self-injury can be considered a form of rumination itself. For example, suicide-specific rumination has been associated with suicide attempts, perhaps because repeated mental rehearsal of suicidal behavior likely leads to a reduction in the innate drive toward self-preservation (Rogers & Joiner, 2018).

Previous work has illustrated potential neurobiological mechanisms of rumination. Specifically, connectivity and task-based activation of the default mode and cognitive control networks (DMN and CCN, respectively, and the interactions between them) has been associated with rumination in adolescents with depression (Burkhouse et al., 2017; Jacobs, Barba, et al., 2016; Jacobs, Watkins, et al., 2016). These neural networks undergo substantial change during the adolescent developmental period (Fair et al., 2008; Koenig et al., 2021). Furthermore, suicidality among adolescents and young adults with depression is associated with disrupted resting-state functional connectivity of regions within these same networks (the DMN and CCN; Westlund Schreiner et al., 2019), potentially high-lighting a shared neural substrate between rumination and self-injury. Preliminary data have shown that targeting the ruminative habit, such as by using rumination-focused cognitive behavioral therapy (RFCBT), may enact change in these specific neural circuits and reduce rumination among adolescents with depression (Jacobs, Watkins, et al., 2016). Furthermore, adolescents who completed RFCBT (and reduced rumination) showed fewer self-injurious behaviors relative to treatment as usual (Bessette et al., 2020).

While there is presently a dearth of studies examining the relationship between rumination and NSSI at the neurobiological level, hypotheses can be informed by research illustrating DMN and CCN connectivity differences among individuals with a history of suicide attempts and/or suicidal ideation (Chase et al., 2017; Stange et al., 2019). These differences may underlie difficulties in the techniques to disengage from the ruminative habit. While not a direct examination of rumination, a study of adolescents with NSSI demonstrated that externally oriented thinking was negatively associated with activation of the supramarginal and inferior frontal gyri (Demers et al., 2019). This effect was observed in response to covert happy faces. In contrast, there was a positive correlation between externally oriented thinking and activation in response to covert fearful faces. This is intriguing because externally oriented thinking is a facet of alexithymia that is negatively correlated with adaptive rumination (Di Schiena et al., 2011).

The present study aims to provide preliminary evidence of how state rumination is represented neurobiologically in relation to past NSSI in a sample of adolescents. State rumination was assessed during a rumination induction task within the scanner, capturing neurobiological characteristics during a heightened emotional state, which may be a precursor to NSSI. We focused specifically on NSSI, as they are discrete behaviors that are typically easier and more reliable to quantify relative to ideation (Millner et al., 2020). We included adolescents with NSSI from a larger study that recruited adolescents with remitted MDD. This study collected information about the number of lifetime NSSI incidents and included acquisition of neuroimaging data during a rumination induction task. We hypothesize that a higher number of NSSI episodes will be associated with greater engagement of regions included in the default mode and salience and emotion networks, including the precuneus, PCC, medial prefrontal cortex, and insula. Furthermore, we anticipate that differences in brain activation will be associated with self-report measures of rumination.

METHOD

Participants

This study was approved by the institutional review board at the University of Utah. We recruited adolescents from Salt Lake City and surrounding areas through radio and Facebook advertisements and community postings. We included data from a larger study that recruited adolescents 14–17 years old with remitted MDD. Exclusion criteria included current depression diagnosis, lifetime history of mania or psychosis, and suicide attempt in the past 6 months. Exclusion criteria (shown in Table 1) also included developmental disorders such as autism spectrum disorders and pervasive developmental delay. ADHD was not an exclusion.

TABLE 1.

Inclusion and exclusion criteria for the larger study and the current sample.

Inclusion criteria

  • 14–17 years

  • Remitted MDD

  • NSSI
Exclusion criteria

  • Suicidal ideation with plan and intent in past 6 months

  • Psychotic disorders

  • Pervasive developmental delay

  • Autism spectrum disorder

  • IQ < 80
Measures

  • KSADS-PL DSM5

  • LSASI

  • RRS

  • MDD diagnosis

  • NSSI lifetime count
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Abbreviations: KSADS-PL, Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version; LSASI, Lifetime Suicide Attempt Self-Injury Count; MDD, Major Depressive Disorder; NSSI, Non-Suicidal Self-Injury; RRS, Rumination Responsiveness Scale.

Measures

Clinical assessment

Upon completing informed consent and assent, participants completed a comprehensive clinician-administered diagnostic assessment including the Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (KSADS-PL; Kaufman et al., 1997). Clinicians also administered the Lifetime Suicide Attempt Self-Injury Count (L-SASII; Linehan & Comtois, 1996) with the adolescent to assess for lifetime NSSI.

In addition to clinician-administered measures, participants completed several self-report questionnaires, including the Ruminative Responses Scale (RRS; Treynor et al., 2003), a 22-item survey that assesses the rumination habit using a 4-point Likert scale (1 = never to 4 = always). The RRS has high internal consistency, acceptable construct validity, and good test–retest reliability (Nolen-Hoeksema, 1991; Treynor et al., 2003). Total RRS scores within our sample had good internal consistency (a = 0.88).

Neuroimaging

We acquired simultaneous-multi-slice (Feinberg & Setsompop, 2013) data using a Siemens 3 T Prisma scanner located at the Imaging and Neurosciences Center at the University of Utah. Scanning protocol included the use of a 32-channel head coil and a high-resolution T1 image that was used for registration purposes a rumination induction task. The T1 was acquired using a voxel size of 0.8 mm isotropic, field of view (FOV) = 256 mm, TE = 2500 ms, TR = 2500 ms, flip angle = 8°, GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) acceleration factor of 2 (Griswold et al., 2002), and a multiband factor of 4. The rumination induction task was acquired using a voxel size of 2.4 mm isotropic, FOV = 216 mm, TE = 30 ms, TR = 800 ms, and flip angle = 52°. We also collected field maps in opposite phase-encode directions for distortion correction.

The Rumination Induction task (Figure 1) is a 9-min block design consisting of rumination and distraction conditions and has been used in previous research with adolescent samples (Bessette et al., 2020; Burkhouse et al., 2017). The entire 9 min of the task was completed in a single run. Prior to entering the scanner, we asked participants to generate events from their own lives that would elicit rumination. These probes were about a failure event, a sad event, a hurtful event, and a frustrating event. Participants rated each event on a 9-point scale to reflect how upset the event made them at the time it occurred and at the current moment (1 = Did not feel sad and 9 = Felt very sad). Participants were asked to generate a new memory if they rated either current or past level of feeling sad about the event as less than a five. Once in the scanner, participants were first asked to bring one of the negative memories to mind in detail (25 s). This was followed by 30 s during which participants are instructed to ruminate about that past event via instructions such as “Think about how sad or happy you are feeling,” “Think about why you reacted the way you did,” and “Think about what your feelings mean.” Participants were subsequently asked to rate “How much are you focused on your feelings right now?” (Rumination question) and “How sad do you feel right now?” (Sadness question) using a scale of 1–5 (1 = not focused on feelings/sad, 5 = very focused on feelings/sad). Following the ratings and 10–15 s of an instruction to “Rest,” participants were presented with a 30 s distraction prompt such as, “Think of a row of shampoo bottles.” Again, participants were asked to rate how focused they were on their feelings and how sad they felt. This series of events was repeated four times during the task.

FIGURE 1.

FIGURE 1

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Rumination induction task. Each full sequence shown above completed 4 times (once for each rumination prompt).

Analyses

Neuroimaging

Neuroimaging preprocessing was performed using command-line executable bash scripts using tools from Anima (Voss et al., 2006) and Statistical Parametric Mapping 12 (SPM12; https://www.fil.ion.ucl.ac.uk/spm/ doc/) software packages. Steps included: echo-planar imaging (EPI) distortion correction, realignment of time-series data, co-registration of high-resolution T1 to time-series, tissue segmentation of high-resolution T1, normalization of high-resolution images to MNI space, normalization of functional images to MNI space, and application of Gaussian smoothing using a 5 mm kernel. We discarded the first 10 volumes of data to ensure magnetization equalization.

Following preprocessing, we completed first level whole-brain activation analyses with the rumination induction data using SPM12. We modeled fixation, distraction, rumination instruction, and rumination prompt and included several different contrasts, with a focus on the contrast of rumination instruction and prompt vs. distraction for second-level analyses. We also included six motion parameters as covariates. We also used the six motion parameters to calculate framewise displacement (FD) values. Participants with greater than 1/3 of their volumes with FD > 0.5 mm were to be excluded from analyses, which is consistent with previous research (Cullen et al., 2014); none were excluded for movement. Consistent with previous research (Crowell et al., 2008; Dillahunt et al., 2022), lifetime self-injury events were natural log-transformed due to high-positive skew (3.67), which yielded a more acceptable skew value (0.57). We used the natural log-transformed lifetime NSSI data as a covariate in our second-level analyses examining the rumination versus distraction contrast. We also incorporated age as a covariate in addition to the mean FD value for each participant. Consistent with the approach in Langenecker et al. (2019), we balanced type I and type II error by using 3dFWHMx within AFNI to estimate the spatial smoothness of the residuals, reporting both -acf and -fwhm options (Cox, 1996; Cox & Hyde, 1997; Langenecker et al., 2019). 3dClustSim was subsequently used to calculate cluster size based on the spatial smoothness estimates generated from 3dFWHMx. Based on a voxel-wise p-value of 0.005 and a cluster-wise p-value of 0.05, we determined a cluster size threshold of 51 voxels and 144 voxels for -fwhm and -acf respectively. We extracted the mean values of any significant rumination vs. distraction contrast clusters that exceed the more liberal threshold of 51 voxels for each participant. We also indicated which of these clusters survive the more conservative threshold of 144 voxels. Additionally, we extracted the means for rumination and distraction blocks separately to aid in visualization and better understanding of how these conditions relate to NSSI.

RESULTS

Demographics and clinical characteristics

A total of 39 participants had complete and usable data and were included in our analyses. Average age of participants was 15.8 (SD = 0.94) and the average number of lifetime NSSI before log transformation was 39.5 (SD = 82.6; range 1–455). Eleven adolescents had a history of at least one suicide attempt (M attempts = 2.91; SD = 3.21), and 4 participants endorsed self-injury episodes with ambivalent suicidal intent (M episodes = 41; SD = 48.45). Further information can be found in Table 2. No participants were excluded due to exceeding the FD threshold of 0.5 mm for over one-third of their volumes (M FD = 0.24; SD = 0.07). Information about significant clusters from the task itself can be found in the Data S1.

TABLE 2.

Sample characteristics.

Demographic characteristics Overall sample
(N = 39)
Age [M (SD)] 15.8 (0.94)
Sex [N (%)]
 Female 26 (66.7%)
 Male 13 (33.3%)
Gender [N (%)]
 Cis female 20 (51.3%)
 Cis male 13 (33.3%)
 Trans female 1 (2.6%)
 Trans male 1 (2.6%)
 Non-binary 3 (7.7%)
 Agender 0 (0%)
 Other 1 (2.6%)
Self-injury and rumination characteristics
 Total lifetime NSSI 39.5 (82.6)
 Total ambivalent suicide attempts (N = 6) 41 (48.45)
 Total suicide attempts (N = 11) 2.91 (3.21)
 RRS total (N = 38) 57.42 (10.57)
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Abbreviations: NSSI, Non-Suicidal Self-Injury; RRS, Rumination Responsiveness Scale.

Task manipulation check

Thirty-eight participants had completed at least one rating for the rumination blocks and 37 had completed at least one rating for the distraction blocks. Paired sample t-tests indicated that level of focus during rumination (M = 3.86, SD = 0.62) was significantly greater than during distraction (M = 3.06, SD = 0.77); t(36) = 9.18, pFDR < 0.001; d = 1.51. Sadness during rumination (M = 3.71, SD = 0.65) was also significantly greater than during distraction (M = 2.70, SD = 0.77); t(36) = 11.50, pFDR < 0.001; d = 1.89. There was no significant difference in reaction times between rating focus during rumination (M = 2684.68 ms, SD = 811.76) versus distraction (M = 2497.97 ms, SD = 718.08), t(36) = 1.51, pFDR = 0.56; d = 0.25. Participants had significantly slower reaction times when providing sadness ratings during rumination (M = 3191.56 ms, SD = 739.19) versus distraction (M = 2746.42 ms, SD = 670.62), t(36) = 4.54, pFDR < 0.001; d = 0.75.

NSSI episodes were not associated with focus or sadness ratings for either rumination or distraction during the task. While more NSSI episodes was negatively associated with reaction times for rating focus on feelings during rumination (r[36] = −0.39, puncorr = 0.02), this did not survive correction for multiple tests (pFDR = 0.12) There were no significant correlations between NSSI and ratings during distraction. These are illustrated in Figure 2. Whereas, participants with more lifetime NSSI gave significantly higher ratings for their past (r[37] = 0.35, puncorr = 0.03) and current level of sadness related to the pre-scan rumination prompt of a time that they had failed badly (r[37] = 0.39, puncorr = 0.01), these did not survive correction for multiple tests (pFDR = 0.11 and 0.10 respectively).

FIGURE 2.

FIGURE 2

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Correlations between lifetime NSSI, RRS scores, and ratings before and during the scan. NSSI, Non-Suicidal Self-Injury; RRS, Ruminative Responses Scale. *p < 0.05; **p < 0.01; ***p < 0.001.

NSSI and rumination induction fMRI

We found a that a greater difference in activation in response to rumination blocks versus distraction blocks was associated with more lifetime NSSI episodes in 11 clusters using a voxel-wise p-value of 0.005, a cluster-wise p-value of 0.05, and a cluster threshold of 51 voxels: (1) right cerebellum (Crus I); (2) right parahippocampal and lingual gyrus; (3) left temporal-occipital fusiform cortex and lingual gyrus; (4) left lateral occipital cortex and precuneus; (5) left superior lateral occipital cortex; (6) left hippocampus and amygdala; (7) left precuneus and posterior cingulate and intracalcarine cortex; (8) left cerebellum (Vermis Crus II); (9) right middle frontal and precentral gyrus; (10) left cerebellum (VI); and (11) left superior and middle frontal gyrus. Table 3 provides additional information about these significant clusters including size, Z statistics, and peak MNI coordinates. Of these clusters, 1 and 2 were also significant using the more conservative cluster threshold of 144 voxels. To get a better understanding of these relations, we generated visualizations of these effects by extracting the activation values of these ROIs from the rumination blocks and distraction blocks separately for each individual. In particular, we were interested in clusters within the DMN. For the two clusters that included the precuneus, the relation between SIBs and activation during the rumination–distraction contrast appear to be driven by a negative correlation between lifetime SIBs and activation during distraction blocks (Figure 3). Regarding the CCN, clusters that included the middle frontal gyrus showed a positive correlation between activation during rumination and lifetime NSSI (Figure 3).

TABLE 3.

Significant rumination—Distraction contrast clusters associated with NSSI.

Region BA MNI Peak Z Cluster k
x y z mm3
aRight cerebellum (Crus I) 44 −48 −40 4.18 179
aRight parahippocampal and lingual gyrus 36 28 −42 −8 4.13 318
Left temporal-occipital fusiform cortex and lingual gyrus 37 −24 −50 −10 4.08 91
Left lateral occipital cortex and precuneus (visual motor) 7 −8 −70 64 4.00 117
Left superior lateral occipital cortex (visual motor) 7 −18 −82 50 3.93 74
Left hippocampus and amygdala −12 −16 −16 3.78 74
Left precuneus and posterior cingulate and intracalcarine cortex 31 −6 −62 16 3.66 95
Left cerebellum (Vermis Crus II) 0 −76 −30 3.49 54
Right middle frontal and precentral gyrus (premotor and supplementary motor areas) 6 44 6 46 3.49 73
Left cerebellum (VI) −2 −78 −18 3.06 74
Left superior and middle frontal gyrus (premotor and supplementary motor areas) 6 −26 10 58 3.03 63
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Abbreviations: BA, Brodmann Area; NSSI, Non-Suicidal Self-Injury; MNI, Montreal Neurological Institute Coordinates.

a

Significant with 3dClustSim -acf cluster threshold of 144.

FIGURE 3.

FIGURE 3

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Association between lifetime NSSI and activation during distraction, and rumination. NSSI, Non-Suicidal Self-Injury.

Additional relations between NSSI, brain activation, and self-report measures

We examined whether SIBs or brain activation in these four significant ROIs were related to self-reported rumination (total score and brooding scale on RRS). There were no significant associations between either rumination scale with lifetime NSSI or with brain activation in any of the 11 ROIs.

DISCUSSION

Rumination is a repetitive negative thought process, or habit, that interacts with negative affect. Rumination typically precedes SIBs and may also follow SIBs in the form of disappointment, guilt, or shame. Neurobiologically, rumination may present as a persistent process that individuals with NSSI find difficult to alter and that could contribute to rigid patterns of thinking. The present study examined the neurobiological relation between SIBs and engagement in rumination. We found that higher lifetime NSSI was associated with a greater contrast in activation between rumination induction and distraction conditions. This was observed in clusters including the precuneus and PCC. Interestingly, this relation was driven by an inverse relation between lifetime SIBs and activation of the precuneus clusters during the distraction condition as opposed to an increased relation during the rumination condition. As NSSI is associated with the ruminative habit, we had anticipated that activation during rumination would associate with lifetime NSSI. Here, instead of potentially experiencing rumination more intensely, individuals with greater lifetime NSSI episodes may instead have greater difficulty disengaging from the ruminative habit (i.e., it is possible that rumination might persist into the distraction condition).

At the self-report level, we found a significant association between maladaptive rumination and ratings of state rumination/sadness during our induction task. However, contrary to predictions, there was no evidence at the neurobiological level that the clusters of activation observed in the rumination versus distraction contrast, which were related to NSSI, were also correlated with RRS scores. Nor was lifetime NSSI and rumination related. Together, these findings support the validity of studying cross-saturation of brain regions that show linear relations with SIBs in the rumination minus distraction contrast.

The precuneus has been implicated in rumination and has shown greater activation during rumination blocks compared with both abstract (e.g., “Think about what contributes to team spirit”) and concrete (e.g., “Think about a row of shampoo bottles on display”) distraction blocks in healthy controls (Cooney et al., 2010). The precuneus plays a key role in the DMN, showing increased activation during rest and tasks involving autobiographical memory and decreased activation during more cognitively demanding tasks (Addis et al., 2004; Raichle et al., 2001; Utevsky et al., 2014). Decreased activation of the precuneus has been associated with script-driven imagery of a self-injury trigger in individuals with NSSI (Kraus et al., 2010) and increased precuneus activation has been positively associated with subjective ratings of relief following a cold stimulus in individuals with NSSI (Osuch et al., 2014). Furthermore, during a self-processing task, adolescents with NSSI demonstrated greater posterior cingulate and precuneus activation relative to adolescents with depression without NSSI and healthy controls (Quevedo et al., 2016). Given this, we would have anticipated that the rumination prompt would associate with decreased precuneus activation, which was not the case. Our finding of reduced activation of DMN-related regions during distraction may suggest that fully engaging in distraction may be more cognitively demanding among adolescents with more lifetime NSSI episodes. Considering that the most common reasons for NSSI typically involve attempts to escape or relieve aversive mental states, individuals with NSSI may find themselves having to employ more cognitive resources when they need to disengage from negative mood states without the aid of self-injury, thereby serving to maintain and reinforce NSSI.

Consistent with prior research implicating the role of the CCN in rumination (Burkhouse et al., 2017; Jacobs, Watkins, et al., 2016), there was a positive relation between lifetime NSSI and the difference in activation during rumination relative to distraction conditions within the middle and superior frontal gyrus. Visualization of these clusters by condition highlighted that this may be driven by increased middle frontal gyrus activation during the rumination blocks among individuals with more lifetime NSSI episodes. The middle frontal gyrus overlaps with both the dorsal and ventral attention regions (Fox et al., 2006), suggesting that with higher lifetime NSSI episodes, individuals tend to have more attentional engagement in the context of repetitive negative thinking. Previous work has also demonstrated greater middle frontal gyrus activation in response to NSSI photographs among adolescent females with NSSI and increased superior frontal gyrus activity during an affective theory of mind task in adults with NSSI (Moon et al., 2022; Plener et al., 2012).

Strengths and limitations

This study provides important information regarding the neural processes associated with rumination in adolescents in relation to past NSSI. In particular, targeting constructs such as attention during negative emotional contexts and facilitating improved cognitive efficiency may be fruitful areas of intervention development. Despite contributing to needed research in the area of SIBs and rumination, this study is limited by what is still a small sample size with respect to broader neuroimaging work, thereby limiting power. Furthermore, there was an insufficient number of participants without SIBs in our sample to represent a psychiatric control group with which to compare these results against. The largest study that this sample was drawn from was also not specifically designed to evaluate SIBs, which is needed to better disentangle the relation between rumination and non-suicidal and suicidal self-injury separately.

Nonetheless, there was a strong association between rumination-related activation and NSSI in our work. Even though this effect in key DMN and CCN regions has appeared in a handful of other studies, this association has been understudied. The lack of inquiry into this topic is unfortunate, as work of this kind may hold promise in the design and implementation of novel interventions targeting NSSI. The need for improved and personalized intervention strategies is underscored by the lack of reduction in SITB prevalence in the general population (Harris et al., 2022). Notably, our efforts to reduce NSSI to date have yet to make a substantial impact. The present study provides preliminary support for a neurobiological representation of rumination as a potential treatment target for NSSI. Future work could build upon this information in an effort to advance innovative, neurobiologically informed approaches to reduce NSSI and associated distress.

Supplementary Material

Supplementary Material

ACKNOWLEDGMENTS

The authors would like to first and foremost thank the participants and their families for contributing their valuable time toward this important work. This research was funded by the National Institute of Mental Health (NIMH) R61 award MH118060 (SAL, ERW) and funding from the University of Utah Neuroscience Program (SAL).

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors have no conflicts of interest.

ETHICS STATEMENT

This study was approved by the Institutional Review Board at the University of Utah.

SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

DATA AVAILABILITY STATEMENT

These data are from a study funded by the National Institute of Mental Health (NIMH) and will be available through the NIMH Data Archive. Data are also available upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material
NIHMS1922494-supplement-Supplementary_Material.docx (20.4KB, docx)

Data Availability Statement

These data are from a study funded by the National Institute of Mental Health (NIMH) and will be available through the NIMH Data Archive. Data are also available upon request.

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