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. Author manuscript; available in PMC: 2026 Jul 29.
Published in final edited form as: Neuropsychologia. 2025 May 9;214:109168. doi: 10.1016/j.neuropsychologia.2025.109168

Reduced mind-wandering and fewer depressive symptoms associated with damage to the medial prefrontal cortex and default mode network

Carissa L Philippi 1,*, Joel Bruss 2,3,4, Carrie Shea 2, Nicholas Trapp 4, Daniel Tranel 2, Aaron D Boes 2,3,4,*
PMCID: PMC12138906  NIHMSID: NIHMS2082577  PMID: 40350145

Abstract

Depressive disorders have been consistently associated with elevated levels of mind-wandering and self-focused negative rumination. Separate tracks of research have implicated brain structures within the default mode network (DMN) in both mind-wandering and depression. In this study, we hypothesized that diminished mind-wandering and fewer depressive symptoms would co-occur in individuals with damage to the DMN. To test this hypothesis, we used a k-means clustering algorithm to identify a target group of patients with reduced mind-wandering and fewer depressive symptoms relative to brain-damaged comparison subjects (n = 37 of 68; ps < 0.001). The anatomical localization of lesions for this target group was predominantly within the medial prefrontal cortex (mPFC). Structural and functional lesion network mapping results revealed that lesions of the target group had significantly greater connectivity with DMN and limbic regions. Taken together, these results suggest that brain injury affecting the mPFC and DMN is associated with both reduced mind-wandering and fewer depressive symptoms. Further investigation of neuroanatomical substrates that mediate a causal relationship between mind-wandering and mood may facilitate the identification of new therapeutic targets for neuromodulation in patients with disorders characterized by maladaptive mind-wandering, such as rumination.

Keywords: mind-wandering, depression, default mode network, medial prefrontal cortex, lesion network mapping

1. Introduction

Mind-wandering, or the process of shifting your attention away from the external environment to focus on your thoughts or feelings, is a common occurrence in everyday life and constitutes up to 50% of each waking day (Kane et al., 2007; Killingsworth and Gilbert, 2010; Smallwood, 2013). Mind-wandering has been used to describe several different but related cognitive processes including thoughts that are self-generated, task-unrelated, or stimulus independent, as well as intentional and unintentional thoughts (Seli et al., 2018, 2016; Smallwood, 2013; Smallwood and Schooler, 2015). Although studies have reported benefits to mind-wandering (Leszczynski et al., 2017; Smallwood and Schooler, 2015), other research suggests that it can also have significant costs to well-being. For example, the amount of time spent mind-wandering is positively correlated with negative mood and depressive symptoms (Chaieb et al., 2022; Figueiredo et al., 2020; Hoffmann et al., 2016; Killingsworth and Gilbert, 2010; Poerio et al., 2013). This has been observed in healthy participants (Killingsworth and Gilbert, 2010; Konu et al., 2021; Smallwood et al., 2007; Stawarczyk et al., 2013; Yamaoka and Yukawa, 2020) and participants with depression (Chaieb et al., 2022 for review). Similarly, individuals with major depressive disorder reported more off-task, self-related, and negative thoughts than comparison participants without depression (Hoffmann et al., 2016). Based on these findings, researchers have proposed that there may be a causal and bidirectional relationship between mind-wandering and depressed mood (Chaieb et al., 2022). Consistent with this hypothesis, results from studies using evidence-based therapies, such as mindfulness-based treatment programs, suggest that focusing on the present and reducing mind-wandering can decrease depressive symptoms and promote well-being (Geschwind et al., 2011; Greenberg et al., 2018; Ramel et al., 2004). On the other hand, inducing negative mood with movie clips or music paired with negative statements has been shown to increase mind-wandering (Smallwood et al., 2009; Smallwood and O’Connor, 2011). Considering this behavioral evidence for a bidirectional interaction between mind-wandering and depressed mood, it is possible that common neuroanatomical substrates may underlie the association of these processes.

Convergent findings from neuroimaging and lesion studies have implicated brain regions within the default mode network (DMN) in mind-wandering, including the medial prefrontal cortex (mPFC), posterior cingulate cortex/precuneus, and inferior parietal lobule (Bertossi and Ciaramelli, 2016; Fox et al., 2015; Philippi et al., 2021; Rosenbaum et al., 2017). At the same time, neuroimaging studies have identified altered activity and connectivity of DMN regions in depression (Greicius et al., 2007; Kaiser et al., 2015; Mulders et al., 2015; Whitfield-Gabrieli and Ford, 2012; Williams, 2017). For instance, greater activity and cerebral metabolism within the subgenual ACC and ventral mPFC has frequently been reported in individuals with major depressive disorder (Drevets et al., 2008; Hiser and Koenigs, 2018; Mayberg, 2003; Pizzagalli, 2011; Pizzagalli and Roberts, 2022). Similarly, several resting-state functional connectivity studies have revealed increased connectivity of the ventral mPFC and other DMN regions in association with depression (Greicius et al., 2007; Kaiser et al., 2015; Mulders et al., 2015; Scalabrini et al., 2020; Williams, 2016). Elevated resting-state connectivity of the DMN has been correlated with depressive symptoms (Y. Li et al., 2023; Pang et al., 2022; Sendi et al., 2021), ruminative thought (Berman et al., 2011; Hamilton et al., 2011; Kaiser et al., 2018; Satyshur et al., 2018), and mind-wandering (Rosenbaum et al., 2017) in depressed populations, though see (Tozzi et al., 2021; Williams, 2016; Yan et al., 2019). Lesion work further indicates that damage of the DMN is associated with reduced mind-wandering (Philippi et al., 2021) and lower self-reported ratings of depressive symptoms (Koenigs et al., 2008; Trapp et al., 2023). Altogether, this research suggests that brain structures within the DMN may be critical for mind-wandering and mood. However, to our knowledge no studies have investigated changes in both mind-wandering and depressive symptoms in the same individuals after brain injury.

To investigate this topic, we first conducted a k-means clustering analysis of individuals with focal, acquired brain lesions to evaluate whether a subset of individuals with focal, acquired brain lesions could be identified with both diminished mind-wandering and fewer depressive symptoms. Our primary hypothesis for this analysis was that individuals meeting both characteristics would have greater connectivity between their lesions and the DMN, as inferred from normative connectome data.

2. Material and methods

2.1. Participants and procedures

Participants with stable and focal brain lesions (n = 68) from the Iowa Patient Registry took part in this study (see Table 1). Subjects were included if they had measures of both depressive symptoms and mind-wandering (described below). All participants were characterized in the chronic phase (>3 months after lesion onset). Neuropsychological and neuroanatomical evaluation were conducted according to the standard protocols of the Benton Neuropsychology Laboratory (Tranel, 2009) and the Laboratory of Human Neuroanatomy and Neuroimaging (Damasio and Damasio, 1989; Frank et al., 1997). Lesion etiology for participants included the following: stroke (n = 41 hemorrhagic or ischemic), surgical resection (n = 11 tumor; n = 6 arteriovenous malformation; n = 8 epilepsy), encephalitis (n = 1 herpes simplex encephalitis) and focal contusion from head trauma (n = 1). We excluded participants with any diagnosed or significant psychiatric or neurological conditions prior to the brain lesion. We verified that there were no patients in the sample who had a diagnosis of major depressive disorder or bipolar disorder prior to lesion onset based a review of each patient’s medical record. Patients could be included if they developed depression after the lesion. It is important to mention that we did not formally diagnose depression in this study. A subset of the participants in this study (n = 22) overlapped with those in a previous lesion study on mind-wandering (Philippi et al., 2021). We also included normative data from nineteen healthy older adults (mean age = 63.6 years ± 7.1 years) from the same prior lesion study (Philippi et al., 2021) to compare mind-wandering for lesion participants in the current study with age-matched individuals without brain injury. All participants gave informed consent according to the protocol approved by the institutional review board at the University of Iowa.

Table 1.

Participant Group Characteristics

Variable Target Group 1 (n = 37) Non-Target Group 2 (n = 15) Non-Target Group 3 (n = 16) p-value
Age (Years) 60.9 (10.8) 48.5 (16.4) 52.1 (13.6) .01 a
Education (Years) 13.8 (2.4) 13.2 (6.1) 14.6 (2.5) .64
Sex 22F/15M 10F/5M 7F/9M .40
Chronicity: Onset to Scan (Years) 2.7 (4.2) 2.0 (2.3) 6.4 (11.1) .35
Chronicity: Scan to IPI (Years) 6.8 (5.1) 8.3 (5.7) 6.7 (4.7) .60
Laterality 14L/11R/12B 10L/4R/1B 6L/7R/3B .18
Lesion Volume (mm3) 47971 (46718) 26741 (19240) 43053 (50657) .41
BDI 4.1 (3.1) 17.9 (3.0) 6.9 (3.4) <.001 b
IPI (%) 41.2 (9.2) 57.3 (16.0) 73.2 (12.5) <.001 c
Cognitive depression 1.1 (1.2) 6.6 (2.8) 2.3 (2.0) <.001 d
WRAT-R: Reading 100.6 (11.3) (n = 28) 99.3 (11.2) (n = 10) 102.6 (10.4) (n = 13) .63
Rey AVLT: Trial 5/30 min recall 11.8 (3.0)/9.0 (3.6) (n = 35) 10.9 (3.3)/9.0 (4.5) 12.9 (1.5)/10.9 (2.4) (n = 15) .27/.24
Token Test 42.2 (4.5) (n = 32) 41.8 (3.2) (n = 12) 41.9 (7.4) (n = 15) .59
TMT-B 82.6 (39.9) (n = 35) 81.2 (48.5) (n = 14) 64.4 (26.0) .40
CFT: 30 min recall 17.4 (7.4) (n = 36) 20.1 (6.4) (n = 55) 21.0 (5.9) .17
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Notes. Means and standard deviations (in parentheses) are reported for demographic and neuropsychological variables. BDI scores included in this table correspond to raw scores. Note, participants were missing values for some of the neuropsychological variables and in those cases n’s are shown in parentheses. All analyses reported in this table compared scores on these demographic and neuropsychological variables between the three groups (target, non-target group 2, and non-target group 3). Chronicity: Onset to Scan was the time between lesion onset and the structural scan and Chronicity: Scan to IPI was the time between the structural scan and the administration of the IPI questionnaire.

a

There were significant group differences in age.

b

There were significant group differences in depression symptoms.

c

There were significant group differences in mind-wandering frequency.

d

There were significant group differences in cognitive depression symptoms based on the subscale (Lee et al., 2018).

There were no significant differences between the target and non-target brain-injured groups in most variables including education, sex, lesion chronicity, lesion laterality, lesion volume, WRAT-R, Rey AVLT Trial 5 or 30 min recall, Token Test, TMT-B, and CFT 30 min recall.

M = male; F = female; Chronicity = time between lesion onset and brain scan in years; Laterality = laterality of lesion; R = right; L = left; B = bilateral; BDI = Beck Depression Inventory-II; IPI = Imaginal Processes Inventory - daydreaming subscale percentage score; WRAT-R = Wide Range Achievement Test-Revised – reading standard score; AVLT = Auditory Verbal Learning Test; TMT-B = Trail Making Test-Part B; CFT = Complex Figure Test.

2.2. Measures

2.2.1. Mind-wandering frequency

We used the 12-item daydreaming subscale of the Imaginal Processes Inventory (IPI) to measure self-reported trait daydreaming frequency (Singer and Antrobus, 1972). The total possible score on the daydreaming scale is 60 with higher scores indicating greater daydreaming frequency. A daydreaming percentage score was used for each subject (e.g., 40% for an IPI score = 24/60) as a measure of mind-wandering frequency.

2.2.2. Depression symptoms

We measured depressive symptoms with the Beck Depression Inventory (BDI; Beck et al., 1996). The BDI is one of the most widely used and validated tools to assess depression symptoms, with high test-retest reliability (Beck et al., 1996; Eser and Asku, 2021). The BDI consists of 21 self-report items used to measure depression-related symptom severity within the past two weeks. In the present study, the BDI score administered closest to the administration of the daydreaming subscale was selected. We also calculated BDI scores for the cognitive depression subscale, which includes symptoms of negative rumination (Lee et al., 2018). Note, BDI-II scores were used for most participants (n = 64), with BDI-I scores included for four participants in the sample.

2.2.3. Additional neuropsychological and personality measures

For descriptive purposes we also compared cognitive, personality and mood assessments between our target and non-target groups across various domains with data available. Prior research has identified relationships between mind-wandering and/or depression and other relevant cognitive, mood, and personality measures. Specifically, studies have reported impairments in working memory (Conradi et al., 2011; Galkin et al., 2020; McIntyre et al., 2015). We assessed working memory performance with the working memory index composite score from the Wechsler Adult Intelligence Scale-IV (WAIS-IV; Wechsler, 2008), which is composed of Digit Span and Arithmetic subtests. Depression tends to be highly comorbid with anxiety (Kessler, 2001; Ruscio and Khazanov, 2017). We measured self-reported anxiety symptoms with the Beck Anxiety Inventory (Beck and Steer, 1990). The Beck Anxiety Inventory is a 21-item scale used to assess physical and cognitive anxiety symptoms within the past week, with total scores ranging from 0 to 63. Previous research indicates that this inventory has high internal consistency, and good reliability and validity (Beck et al., 1988; Fydrich et al., 1992; Hewitt and Norton, 1993). Other research indicates that higher levels of self-reflection or mind-wandering are associated with improved self-insight in healthy populations (Hixon and Swann, 1993; Sedikides and Skowronski, 1995; Trapnell and Campbell, 1999). In addition, studies have reported positive correlations between apathy and depression (Batail et al., 2018) and greater apathy has been reported in individuals with damage overlapping with the mPFC of the DMN (Jenkins et al., 2022; Raimo et al., 2019). To examine levels of self-insight and apathy, we used the Iowa Scales of Personality Change, which provides an assessment of 26 personality characteristics that might change as a result of brain injury, including traits related to emotional functioning, social and interpersonal behavior, decision-making and goal-directed behavior, behavioral control, and insight (Barrash et al., 2011, 2000). For this inventory, ratings are given by a collateral person who has had regular and meaningful contact with the participant prior to the lesion onset and at the time of testing. In the current study, we used the “Change” rating which describes the change in the characteristic after brain injury and ranges from –4 (less of the characteristic) to 4 (extreme change). For the purposes of these analyses, we reversed the scoring for self-insight to provide ease of interpretation with the score reflecting changes in levels of self-insight, with lower scores indicating worse self-insight and higher scores corresponding to better self-insight after brain injury. The change score for apathy was not reversed, with higher scores indicating more apathy after brain injury.

We also tested participants on several neuropsychological measures including word identification and reading (Wide Range Achievement Test-Revised: Reading Standard Score), verbal memory (Rey Auditory Verbal Learning Test: scores for Trial 5; 30 min delayed recall), language (Token Test score from the Multilingual Aphasia Examination), executive functioning (Trail Making Test-Part B), and visuospatial memory (Complex Figure Test: 30 min delayed recall score). We examined these neuropsychological measures to determine whether there were group differences in these general cognitive domains.

2.3. Lesion mapping procedures

Structural scans were collected in the chronic epoch (>3 months after lesion onset) for all participants using MRI acquired T1-weighted and T2-weighted images or a CT scan for participants with MRI contraindications. Lesion masks created before 2006 were generated using the MAP-3 method, which consists of the manual tracing of lesion borders for a given subject onto a normal reference brain using the lesion depicted in the subject’s MRI or CT scan (Fiez et al., 2000; Frank et al., 1997). Due to advances in automated methods for spatial transformation, lesion masks produced after 2006 were manually traced on the native T1-weighted scan for each subject using FSL and then transformed to MNI template space as described in our recent work (Trapp et al., 2023). Spatial normalization was applied to align all lesion masks to the same MNI template space using algorithms in ANTs that can improve the accuracy of normalization for brain images with focal lesions (Avants et al., 2008; Brett et al., 2001; Nachev et al., 2008; Trapp et al., 2023). The accuracy of the lesion tracing and normalization was verified by a neurologist (A.D.B.) by examining the lesion in both the native and MNI template space without knowledge of the behavioral outcomes.

2.4. Clustering analysis

We conducted a k-means clustering analysis in R using the purrr and cluster packages to determine whether any unique groups were identified with both reduced mind-wandering frequency and fewer depression symptoms. We used three different summary statistics to evaluate different clustering models including an elbow plot, silhouette plot, and gap statistic (using 500 bootstrap iterations). We determined the best fit of models based on agreement of two or three of the summary measures. We ran models with one to ten clusters for evaluation and plotted the summary statistics.

2.5. Lesion-deficit proportional subtraction analysis

To compare the lesion localization for the target and the non-target groups from the k-means clustering analysis, we conducted a mass-univariate lesion-deficit proportional subtraction analysis using the Iowa Brain-Behavioral Modeling Toolkit (Griffis et al., 2024). This non-parametric permutation-based correction method was developed to improve options for multiple comparisons correction for voxelwise lesion symptom mapping (Mirman et al., 2018). We generated proportional overlaps for each group (target and non-target) by summing binary lesion masks within each group and dividing by the total number of subjects in each group. The resulting proportional overlap map for the target group was then subtracted from the proportional overlap for the non-target group, resulting in a proportional difference map. We also conducted three other proportional subtraction analyses to compare the target group against each non-target group as well as to compare the two non-target groups to provide additional information about group-level differences. To determine the significance of the inferential model compared to an empirical null distribution of model fits, permutation testing was performed using 1000 permutation iterations. The familywise error (FWE) method (Mirman et al., 2018) was used to determine a single family-wise error threshold across all predictors at predictor threshold of v = 1000 corrected at pFWE < .05.

2.6. Functional and structural lesion network mapping analyses

Lesion network mapping is a method used to infer how lesions may disrupt network connectivity using normative connectome data (Boes et al., 2015; Philippi et al., 2021). We performed three different lesion network mapping analyses. First, we qualitatively evaluated the brain-wide functional connectivity map seeded from the location of the significant voxels in the lesion-deficit proportional subtraction analysis. This resting-state functional connectivity analysis used the GSP1000 normative dataset. Second, to quantitatively evaluate network connectivity differences between target and non-target groups we used Permutation Analysis of Linear Models (PALM) implemented in FSL as in previous studies (Joutsa et al., 2022a, 2022b; Pini et al., 2021; Trapp et al., 2023), we created a functional lesion network map for each participant by using each individual lesion mask as a ‘seed’ region in a resting-state functional connectivity analysis. This produces a brain-wide network map with each voxel representing the strength of connectivity to the lesion. Each analysis used resting-state functional connectivity from the GSP1000 normative dataset. When generating networks from the lesion seeds we use the principal component method (as in Pini et al., 2021). Third, we generated a structural lesion network map for each participant by using each individual lesion mask as a ‘seed’ region to create tractography streamline maps from the Human Connectome Project normative dataset (as in Gelineau-Morel et al., 2025). For both functional and structural lesion network maps, we used a two-tailed voxelwise general linear model with 10,000 permutations and examined results at uncorrected and FWE-corrected p < .05 threshold implemented in FSL Permutation Analysis of Linear Models (Trapp et al., 2023; Winkler et al., 2014). Finally, we visually compared the results from our lesion network mapping and lesion-deficit proportional subtraction analyses with the 7-network cortical parcellation (Yeo et al., 2011) to further describe the overlap of our lesion findings with cortical network(s).

3. Results

Using a k-means clustering analysis, we identified three cluster groups that differed based on their levels of mind-wandering and depression symptoms (Figure 1). Both the elbow and silhouette plots revealed optimal performance for three clusters. Only the gap analysis suggested that two clusters were optimal. In both the two and three cluster models, there was a cluster group with both low mind-wandering frequency and low depression. Importantly, this cluster group only differed by one subject between the two models. We selected the three-cluster model given that it displayed optimal performance across two of the three summary measures (Figure S1). Specifically, we found a target group (cluster 1) of patients with focal brain lesions (n = 37 of 68) that had reduced mind-wandering and fewer depressive symptoms relative to the non-target cluster groups 2 and 3 with brain injury (ps < .001, Table 1).

Figure 1. Target group with reduced mind-wandering and depression symptoms.

Figure 1.

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Results from k-means clustering analysis. The scatterplot shows that the target group (cyan) had lower mind-wandering and fewer depression symptoms than the non-target cluster groups 2 (blue) and 3 (yellow).

In addition, the target group reported fewer cognitive symptoms of depression when compared with the non-target cluster groups 2 and 3 (p < .001). Across the sample, there was a significant positive correlation between mind-wandering and total depressive symptoms (rs(68) = .28, p = .02). We found no differences between the target group and the non-target groups in the strength of this correlation between mind-wandering and depression symptoms (ps = .25-.58).

Next, we compared the target group to nineteen healthy older adults from a prior study (Philippi et al., 2021), the target group had significantly lower mind-wandering on average than the healthy comparison group (M = .41 and M = .65, U = 96, p < .0001. The target group had a mean BDI-II score of 4.1 ± 3.1, which is lower than previously published mean BDI-II scores of 8.6 ± 7.7 reported for community dwelling older adults (Segal et al., 2008). Together, these results suggest that the target group had reduced mind wandering and depressive symptoms when compared with healthy older adults.

There were no significant differences between the target and non-target groups for most demographic, clinical, and neuropsychological variables including education, sex, lesion chronicity, lesion laterality, lesion volume, word identification and reading, verbal memory, language, executive functioning, and visuospatial memory (ps = .17-.64; Table 1). However, there were significant differences between these brain-injured groups in age (p = .013). When comparing the non-target groups 2 and 3, we found no significant differences in any demographic, clinical, or neuropsychological variables (ps = .12-.86). Thus, our lesion and descriptive analyses below compared the target group with a combined non-target group (cluster groups 2 and 3). To account for group differences in age, descriptive analyses were reported with and without age as a covariate.

Evaluation of the lesion location of the target group showed the peak site of lesion overlap within the mPFC (Figure 2A). The voxelwise proportional subtraction lesion-deficit analysis with permutation-based FWE-correction showed that the target group had greater lesion overlap within the ventral mPFC of the DMN and limbic network than the non-target cluster groups 2 and 3 (Figure 3A, Figure S2). When comparing the non-target groups using proportional subtraction, we found that non-target cluster group 2 had greater lesion overlap with the left medial temporal lobe than the non-target cluster group 3 (Figure S2). Next, we qualitatively evaluated the functional connectivity network derived from the brain region showing maximum association with the target group based on lesion location. In line with our hypothesis, this network pattern had strong connectivity with DMN and limbic regions, including the ventral mPFC, posterior cingulate cortex/precuneus, lateral temporal cortex, and inferior parietal lobule (Figure 3B). Next, we conducted lesion network mapping analyses to identify which regions had higher connectivity with the target group compared to the non-target group lesions. This was done with functional and structural connectome data. Consistent with our hypothesis, we found that the lesions of the target group demonstrated higher connectivity than the non-target group with several DMN regions, including the ventral mPFC, precuneus, lateral temporal cortex, and inferior parietal lobule than the non-target groups (Figure 4). No network results survived FWE multiple comparisons correction.

Figure 2. Lesion overlap maps for target and non-target groups.

Figure 2.

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(A) Lesion overlap map for the brain-injured target cluster 1 group with reduced mind-wandering and depressive symptoms shows a high proportion of lesions in the ventral mPFC. (B) Lesion overlap for the non-target cluster group 2. (C) Lesion overlap for the non-target cluster group 3. Color bars correspond to the number of overlapping lesions for each group, ranging from the minimum to the maximum overlap of lesions.

Figure 3. Lesion-deficit analysis and lesion network mapping shows target group with greater overlap with DMN.

Figure 3.

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(A) The target group had a significantly higher proportion of lesions in the ventral medial prefrontal cortex relative to the non-target groups. This is shown here with voxelwise proportional subtraction lesion-deficit map, with red regions reaching significance with permutation-based FWE-correction (p < 0.05). (B) Using the significant region in panel A to ‘seed’ a resting-state functional connectivity analysis with normative connectome data, this image shows connectivity of this mPFC region with the DMN and limbic regions including the ventral mPFC, posterior cingulate cortex/precuneus, lateral temporal cortex, and inferior parietal lobule.

Figure 4. Functional and structural lesion network maps consistent with other lesion analyses.

Figure 4.

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Functional (A) and structural (B) lesion network mapping analyses revealed that lesions of the target group had higher connectivity with several DMN regions, including the ventral mPFC, precuneus, lateral temporal cortex, and inferior parietal lobule than the non-target cluster groups 2 and 3. For both panels, uncorrected p < .05 results are shown with values ranging from dark red (higher connectivity in target group) to dark blue (higher connectivity in non-target groups) and raw t-stats range from light red to light blue. No network mapping results survived FWE correction for multiple comparisons.

Lastly, we investigated whether the target group exhibited differences in other relevant cognitive, mood, and personality trait measures. The target group reported fewer anxiety symptoms than the non-target groups (U = 70.00, p = .014, Table 2). This effect for anxiety symptoms remained significant after covarying for age (p = .016, Table 2). Although, on average, working memory scores were higher, self-insight ratings were lower, and there was higher post-lesion apathy in the target group, there were no significant group differences in these measures (ps = .19-.81, Table 2).

Table 2.

Other Relevant Cognitive, Mood, and Personality Measures

Group Target Group Non-Target Group p-value without age p-value with age
Working memoryb 101.9 (16.9) 97.5 (17.1) .19 .32
Anxietya 3.7 (3.8) 7.9 (5.3) .01 .02
Self-Insightb −0.05 (1.9) 0.47 (1.2) .57 .82
Apathyb 0.64 (1.5) 0.53 (1.5) .81 .69
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Notes. Means and standard deviations (in parentheses) are provided for all measures.

a

The target group reported significantly fewer anxiety symptoms than the non-target group even after controlling for age.

b

There were no significant group differences in working memory, levels of self-insight, or apathy after brain injury. These non-significant results remained the same after controlling for age.

Self-Insight scores reported in this table were reverse coded using the lack of insight “Change” ratings from the Iowa Scales of Personality Change, with lower scores indicating lower levels of self-insight after brain injury.

Note, we were missing the Beck Anxiety Inventory and Iowa Scales of Personality Change for several participants in the target group (missing n = 17 for Beck Anxiety Inventory and n = 15 for Self-Insight and Apathy) and non-target group (missing n = 17 for Beck Anxiety Inventory and n = 14 for Self-Insight and Apathy).

4. Discussion

In this paper we investigated whether reduced mind-wandering and fewer depressive symptoms were associated with damage to the DMN in a cohort of lesion patients. Using a k-means clustering analysis we identified a target group of individuals with both reduced mind-wandering and depressive symptoms, including negative rumination symptoms. Lesions of the target group occurred at a higher rate in the ventral mPFC, near the border of DMN and limbic nodes of the mPFC. For structural and functional lesion network mapping analyses, we found that the target group had disruption of the DMN, as evidenced by greater lesion connectivity to the inferior parietal lobule, precuneus, and lateral temporal cortex than the non-target groups. We also observed that this target group had fewer anxiety symptoms. This suggests that reduced mind-wandering and fewer depression and anxiety symptoms are associated with damage to DMN and limbic regions.

These findings are aligned with prior research showing positive correlations between mind-wandering and depressive symptoms in individuals with and without major depressive disorder (Figueiredo et al., 2020; Hoffmann et al., 2016; Poerio et al., 2013). Our results further provide support for the proposed reciprocal relationship between mind-wandering and depressed mood, as both of these processes were diminished in the target group after brain injury. If this relationship is reciprocal, it may help explain why it can be so difficult to break out of the persistent cycle of mind-wandering about distress and negative aspects of oneself that can contribute to the onset and maintenance of depression (Chaieb et al., 2022; Kube and Glombiewski, 2021; Nolen-Hoeksema et al., 2008). Recent research has begun to quantify this persistent and sticky relationship between mind-wandering and mood, showing more sustained periods (lower variability) of self-related and negative thoughts in individuals with depression (Hoffmann et al., 2016). Together, these findings suggest that prolonged mind-wandering that is self-related and has a negative emotional tone may be highly related to ruminative cognition or perseverative thought. Aligned with this hypothesis, the target group also had fewer cognitive depression symptoms, including self-criticalness, worthlessness, hopelessness and guilt, which have been associated with negative rumination (Kim et al., 2011; Robinson and Alloy, 2003). Alternatively, it is also possible that the non-target cluster groups had higher depressive symptoms due to greater functional impairment and cognitive symptoms than the target group. Though, we did not observe any differences in neuropsychological or cognitive measures between the non-target and target groups in our study. Nevertheless, additional research is necessary to investigate the similarities and differences between mind-wandering and rumination as well as possible relationships with functional impairments in depression.

The lesion location in this target group was primarily localized to the ventral portion of the mPFC. Our structural and functional lesion network mapping analyses showed that the target group had brain injury primarily affecting connectivity with DMN regions. These findings are consistent with functional neuroimaging and lesion studies separately implicating the mPFC and DMN regions in mind-wandering (Bertossi and Ciaramelli, 2016; Fox et al., 2015; Philippi et al., 2021) and depression (Drevets et al., 2008; Kaiser et al., 2015; Koenigs et al., 2008; Mayberg, 2003; Pizzagalli, 2011; Trapp et al., 2023; Williams, 2016). Besides DMN regions, our lesion analyses also revealed higher lesion connectivity with limbic regions for the target group (Yeo et al., 2011), in particular the orbitofrontal cortex. The orbitofrontal cortex has been reliably associated with the pathophysiology of depression (Drevets, 2007; Mayberg, 1997; Rolls et al., 2020). For example, individuals with depression tend to exhibit greater activity and connectivity of orbitofrontal regions and reduced activity and connectivity after successful treatment response (Drevets, 2007; Rolls et al., 2020). Altogether, our results are novel in showing that both mind-wandering and depressive symptoms were reduced in individuals with lesions to the DMN and limbic regions, though we cannot infer whether these two concurrent observations are secondary to a common neural mechanism. Given that we examined mind-wandering frequency based on a trait daydreaming scale, another important question for future lesion research is whether different brain regions within the DMN and limbic network contribute to certain aspects of mind-wandering content, intentionality, or dynamics.

In our descriptive analyses, we found that the target group exhibited fewer anxiety symptoms than the non-target groups. These results are aligned with reported co-morbid anxiety in those with depression (Kessler, 2001; Ruscio and Khazanov, 2017). Consistent with neuroimaging literature in depression, anxiety disorders have also been associated with elevated activity and connectivity of DMN regions, including the mPFC and inferior parietal lobule (W. Li et al., 2023; Yuan et al., 2023). Several studies have further shown positive correlations between anxiety symptom severity and connectivity between DMN regions in individuals with generalized anxiety disorder (W. Li et al., 2023; Yuan et al., 2023). In addition, worry and perseverative cognition have been associated with elevated mPFC activity and DMN connectivity (Gerlach et al., 2021; Makovac et al., 2020; Mohlman et al., 2017; Servaas et al., 2014). Together with our study findings, this suggests that DMN regions may be important for perseverative cognition associated with both depression and anxiety. Though, future research will be required to replicate these findings in a larger sample.

From a clinical standpoint, our results could also be relevant to treatment interventions for depression that reduce mind-wandering, negative rumination, and/or DMN activity. For example, a few studies have shown decreases in both mind-wandering and depressive symptoms after mindfulness-based treatments in individuals with and without depression (Greenberg et al., 2018; Kawashima et al., 2022; Takahashi et al., 2020). Other research using non-invasive brain stimulation techniques targeting DMN or frontoparietal network regions have shown some efficacy in either reducing mind-wandering (Bertossi et al., 2017; Kajimura et al., 2016; Kajimura and Nomura, 2015; Nawani et al., 2023) or attenuating depressive symptoms (Avissar et al., 2017; Cash et al., 2019; Cole et al., 2020; Fox et al., 2012; Liston et al., 2014; Weigand et al., 2018). Convergent with our lesion-derived network mapping results, neuroimaging findings further suggest that reduced activity and connectivity of DMN regions, in particular the mPFC, accompany the diminished mind-wandering (Bertossi et al., 2017; Kajimura et al., 2016) and depressive symptoms (Cash et al., 2019; Fox et al., 2012; Liston et al., 2014; Weigand et al., 2018) after brain stimulation. However, some recent studies using either transcranial direct current stimulation or repetitive transcranial magnetic stimulation have also reported increased mind-wandering or no change in mind-wandering after inhibitory or excitatory stimulation targeting the left dorsolateral prefrontal cortex (Aasen et al., 2024; Filmer et al., 2019; Nawani et al., 2023). Considering these mixed findings in healthy populations, additional randomized control trials are needed to understand the effects of brain stimulation to DMN and frontoparietal network regions on mind-wandering, depressed mood, and DMN connectivity in depression.

Although the present study had several strengths, there were also some important limitations to discuss. First, we measured mind-wandering using a self-report trait daydreaming questionnaire. An interesting question for future research will be to determine how brain injury affecting DMN regions alters the content of mind-wandering or intentional versus unintentional mind-wandering in addition to depressed mood. Considering the association between past-oriented mind-wandering, negative mood, and depressive symptoms in those with and without depression (Hoffmann et al., 2016; Ruby et al., 2013; Smallwood et al., 2007; Smallwood and O’Connor, 2011), it is possible that DMN lesions may also reduce the frequency of negatively valenced thoughts about the past. Second, we are reporting on an interesting co-occurrence between reduced mind-wandering and depressive symptoms and lesions to the DMN as opposed to making a causal claim. Given that we did not examine the content of mind-wandering, it is possible that the affective valence of internal thoughts could differentially relate to depressive symptoms. For example, mind-wandering involving more positive thoughts, memories, and emotions may relate to fewer depressive symptoms whereas mind-wandering about more negative thoughts, memories, or emotions may be associated with higher depressive symptoms. In addition, although we observed decreased mind-wandering and fewer depressive symptoms in the target group, we are not trying to claim that all aspects of DMN damage are positive. Third, we had a smaller sample of participants with anxiety symptom scales as well as collateral ratings of self-insight and apathy. Further, we only had collateral ratings of self-insight and apathy as opposed to self-ratings which could have been used to derive an index of impaired self-awareness. Although we identified lower average self-insight and a greater change in apathy in the target group, we did not find significant group differences between these variables. Thus, subsequent research in a larger sample will be needed to examine changes in anxiety, insight, apathy, and impaired self-awareness following lesions affecting the DMN. Fourth, we used lesion-derived network mapping to determine how brain injury altered connectivity with the DMN. Although this network mapping approach provides considerable advantages, it also relies on resting-state data from healthy participants without brain injury. In addition, it requires larger sample sizes and neuroimaging data may only be available after brain injury, such as in the case of a stroke. Thus, future research with a larger sample size will be necessary to investigate changes in resting-state functional connectivity of DMN regions in participants before and after brain injury. Fifth, we used the BDI-II which measures self-reported depressive symptoms over the past two weeks. Given that damage to the mPFC or DMN can influence emotion-related processing and self-referential cognition (Goel et al., 2017; Koenigs et al., 2008; Philippi et al., 2011; Trapp et al., 2023), it is possible that such damage in our target group could have affected the ability to accurately report on their own depressive symptoms. Though, we did not find evidence of significant differences in self-insight in the present study. In addition, we excluded participants with any significant psychiatric disorder or a diagnosis of major depressive disorder or bipolar disorder prior to lesion onset. Therefore, additional research will be needed to replicate our findings using other clinician administered depression symptom measures and including participants with a premorbid diagnosis of a depressive disorder. Sixth, although there were no significant differences between target and non-target groups in lesion volume or chronicity, there was variability in these measures across groups. To address this limitation, future research in larger samples could match lesion groups for lesion volume and chronicity.

5. Conclusion

In summary, our results indicate that brain injury affecting the mPFC is associated with reduced mind-wandering and fewer depressive symptoms, and this may be due to DMN dysfunction. This target group also had fewer anxiety symptoms than brain-injured comparisons, suggesting that decreasing mind-wandering could improve negative mood associated with depression and anxiety. Further investigation of neuroanatomical substrates that mediate a causal relationship between mind-wandering and mood may facilitate the identification of new therapeutic targets for neuromodulation in patients with disorders characterized by maladaptive mind-wandering, such as rumination.

Supplementary Material

1

Highlights.

  • We examined whether damage to the DMN would affect both mind-wandering and mood

  • Clustering analysis identified a target group with reduced mind-wandering and depression

  • Target group had mPFC damage and greater connectivity with DMN and limbic regions

  • Results could inform therapeutic targets for disorders with maladaptive mind-wandering

Acknowledgements

This research was partially supported by the National Institute of Health (P50 MH0942581, R01 NS114405) and the Kiwanis International Neuroscience Research Foundation. We recognize the important contributions of the participants in making this research possible.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Data Availability Statement:

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Supplementary Materials

1
NIHMS2082577-supplement-1.docx (4.5MB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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