Distinct individual differences in default mode network connectivity relate to off-task thought and text memory during reading

Often, as we read, we find ourselves thinking about something other than the text; this tendency to mind-wander is linked to poor comprehension and reduced subsequent memory for texts. Contemporary accounts argue that periods of off-task thought are related to the tendency for attention to be decoupled from external input. We used fMRI to understand the neural processes that underpin this phenomenon. First, we found that individuals with poorer text-based memory tend to show reduced recruitment of left middle temporal gyrus in response to orthographic input, within a region located at the intersection of default mode, dorsal attention and frontoparietal networks. Voxels within these networks were taken as seeds in a subsequent resting-state study. The default mode network region (i) had greater connectivity with medial prefrontal cortex, falling within the same network, for individuals with better text-based memory, and (ii) was more decoupled from medial visual regions in participants who mind-wandered more frequently. These findings suggest that stronger intrinsic connectivity within the default mode network is linked to better text processing, while reductions in default mode network coupling to the visual system may underpin individual variation in the tendency for our attention to become disengaged from what we are reading.

were required to note down any moments when they noticed they had stopped paying attention to the meaning of the text, by circling the word they had reached at this point. A detailed instruction booklet was used to guide the participants through the experiment. After they finished reading, they were asked to answer 17 open-ended questions, without being able to refer back to the text 45 . For some questions, the answers were explicitly presented in the text (i.e., How high was the annual membership fee for the geological society? or How old did Holmes estimate the earth to be? or What kind of interim business did Holmes run to be able to support his family?), while others required text memory but also required participants to retrieve and integrate semantic information to support their understanding of the paragraph and to answer the question (i.e., Why does the author compliment about Holmes first estimation of the age of the earth as 'quite an achievement'? or Which circumstance was responsible for delaying Holmes' work?). The questions did not require discourse-level comprehension.
The answers to the questions were scored for accuracy by two experimenters. Responses were given a score of 1 if they contained key information, and otherwise a score of 0. The two scorers produced very similar ratings (r = 0.92, p < 0.001). The prior knowledge of geology was also assessed by two questions: (1) Have you previously studied Geology, and if so for how many years? (2) Please indicate how much you already knew about the content of the Geology text prior to reading the text based on a 0 to 10 Likert scale. The participants stated that they were unfamiliar with the content of the text (M ± SD = 1.47 ± 1.75) and had almost no Geology study during their education (M ± SD = 0.06 ± 0.29 year). There was also no correlation between these ratings with either reading assessment or off-task experiences (ps > 0.1). The experiment took approximately 30 (±5) minutes.
Off-task experiences. A self-report measurement, the New-York Cognition Questionnaire (NYC-Q), was also used to assess off-task behaviour during the reading task. The first section contained 22 questions about the content of thoughts (e.g., I thought about personal worries), rated on a scale of 1 (Completely did not describe my thoughts) to 9 (Completely did describe my thoughts). The second section contained 8 questions about the form of these thoughts (e.g., whilst I was reading my thoughts were in the form of images), rated on a scale of 1 (Completely did not characterize my experience) to 9 (Completely did characterize my experience) 3,55 . In the current study, we limited our analysis to the 22 questions relating to the content of off-task thought. We calculated an overall average for each participant, which is thought to reflect how much each individual was thinking off-task thoughts. In this way, we assessed both off-task frequency (i.e., the number of moments when attention was not directed towards the reading task) and the content of these experiences.
Prior to data analysis, all variables were z-transformed and outliers more than 2.5 standard deviations above or below the mean were identified. Using this criterion, there was one outlier for text-based memory, three outliers for off-task frequency and no outliers in the content of off-task thought questionnaire. These outlying values were imputed with the cut-off value (i.e., +/−2.5 standard deviations above or below the mean). No participant was removed from the data analysis as a result of this process.
Neuroimaging data acquisition. Structural and functional data were acquired using a 3T GE HDx Excite Magnetic Resonance Imaging (MRI) scanner utilizing an eight-channel phased array head coil at the York Neuroimaging Centre, University of York. Structural MRI acquisition in all participants was based on a T1-weighted 3D fast spoiled gradient echo sequence (repetition time (TR) = 7.8 s, echo time (TE) = minimum full, flip angle = 20°, matrix size = 256 × 256, 176 slices, voxel size = 1.13 mm × 1.13 mm × 1 mm).
The sentence-reading task in Experiment 1 used single-shot 2D gradient-echo-planar imaging (TR = 3 s, TE = minimum full, flip angle = 90°, matrix size = 64 × 64, 60 slices, voxel size = 3 mm × 3 mm × 3 mm, 80 volumes). The participants passively viewed meaningful sentences (e.g., her + secrets + were + written + in + her + diary) and meaningless sequences of nonwords (e.g., crark + dof + toin + mesk + int + lisal + gl od + flid), item-by-item. In total, there were 10 meaningful sentences, taken from Rodd, et al. 56 , and 10 nonword lists, matched for both word length and number of syllables. Word and nonword sets were each presented in two blocks in a pseudo-random order (i.e., a total of 4 blocks). A task instruction (e.g., Meaningful) was used to indicate the transition between different conditions. Each sequence ended with a red fixation lasting 4000-6000 ms. Each word or nonword was presented for 600 ms, followed by a 250 ms fixation before the next item was presented. A fluid-attenuated inversion-recovery (FLAIR) scan with the same orientation as the functional scans was collected to improve co-registration between subject-specific structural and functional scans.
A 9-minute resting-state fMRI scan was used in Experiment 2, recorded using single-shot 2D gradient-echo-planar imaging (TR = 3 s, TE = minimum full, flip angle = 90°, matrix size = 64 × 64, 60 slices, voxel size = 3 mm × 3 mm × 3 mm, 180 volumes). During resting-state scanning, the participants were instructed to focus on a fixation cross with their eyes open and to keep as still as possible, without thinking about anything in particular. Neuroimaging data for Experiments 1 and 2 were collected in the same session, with the resting-state sequence presented first, so that measures of intrinsic connectivity could not be influenced by the words and nonwords that were presented in the sentence-reading task. There was a break of a few minutes between these scans which allowed us to remind participants of the task requirements and set up the scanning.
Neuroimaging data pre-processing. All functional and structural data were pre-processed using a standard pipeline and analysed via the FMRIB Software Library (FSL version 6.0, www.fmrib.ox.ac.uk/fsl). Individual FLAIR and T1-weighted structural brain images were extracted using FSL's Brain Extraction Tool (BET). Structural images were linearly registered to the MNI152 template using FMRIB's Linear Image Registration Tool (FLIRT). The sentence-reading functional neuroimaging data were pre-processed and analysed by using FSL's FMRI Expert Analysis Tool (FEAT). A standard pre-processing pipeline was applied, including motion correction via MCFLIRT, slice-timing correction using Fourier space time-series phase-shifting, and spatial smoothing using a Gaussian kernel of FWHM 6 mm. In addition, for the task-based fMRI data in Experiment 1, high-pass temporal filtering (sigma = 100 s) was applied in order to remove temporal signal drift. For the resting-state fMRI (2019) 9:16220 | https://doi.org/10.1038/s41598-019-52674-9 www.nature.com/scientificreports www.nature.com/scientificreports/ data in Experiment 2, both high-pass (sigma = 200 s) and low-pass temporal filtering (sigma = 2.8 s) were applied, in order to constrain analyses to low-frequency fluctuations.

Neuroimaging analysis. Task-based fMRI analysis (Experiment 1). This analysis identified sites in
which activation during the sentence reading task was modulated by individual differences in text-based memory, off-task frequency or the content of off-task thought (i.e., NYC-Q), measured outside the scanner. In the first-level analysis of the sentence reading task performed in the scanner, we identified voxels responding to (i) meaning and (ii) orthographic inputs devoid of meaning, through the contrasts of Meaningful > Baseline, Meaningless > Baseline, Meaningful > Meaningless, plus the reverse, for each participant. In the higher-level analysis at the group level, z-transformed behavioural data for text-based memory, off-task frequency and NYC-Q were added as explanatory variables, using FMRIB's Local Analysis of Mixed Effects (FLAME1), with automatic outlier de-weighting 57 . A 50% probabilistic grey-matter mask was applied. Clusters were thresholded using Gaussian random-field theory, with a cluster-forming threshold of z = 2.6 and a familywise-error-corrected significance level of p = 0.05.
Resting-state fMRI analysis (Experiment 2). We next considered whether the intrinsic connectivity of regions identified in Experiment 1 predicted text-based memory and off-task thought. A cluster which showed a stronger response to orthographic input in people with good text-based memory overlapped with (i) the default mode network (DMN), which is implicated in both reading comprehension and spontaneous thought 46,47,58 , (ii) the adjacent frontoparietal network (FPN), which plays a central role in cognitive control 59 , as well as (iii) the dorsal attention network (DAN), which supports externally-directed attention 60 . We therefore masked the results of Experiment 1 by these DMN, FPN and DAN networks, defined by a parcellation of 1000 resting-state scans 61 , obtained from Freesurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/CorticalParcellation_Yeo2011). This identified a region of middle temporal gyrus (MTG) within DMN, and a region of inferior temporal gyrus (ITG) in the FPN, as well as another region of inferior temporal region (ITG)/lateral occipital cortex (LOC) in the DAN. These regions were taken as seeds in a subsequent analysis of intrinsic connectivity.
We extracted the time series from the seeds and used this data as explanatory variables in whole-brain connectivity analyses at the single-subject level. Sixty-four participants were included in this analysis (five participants without intrinsic connectivity data were excluded). These functional connectivity maps were then related to individual differences in behaviour using a multiple regression model, in which z-transformed scores for text-based memory, off-task frequency and NYC-Q were added as explanatory variables. In order to control for the spurious correlations that might emerge from movement, we included two canonical components, group mean and mean framewise displacement (FD) 62 , as nuisance covariates in the model. Automatic outlier de-weighting was used and a 50% probabilistic grey-matter mask was applied. Clusters were thresholded using Gaussian random-field theory, with a cluster-forming threshold of z = 2.6 and a familywise-error-corrected significance level of p = 0.05. We also applied Bonferroni correction to account for the fact that we included three models (ITG within FPN, ITG/LOC within DAN, and MTG within DMN) and used two-tailed tests (in which behaviour could relate to both stronger and weaker connectivity). Consequently, the p-value accepted as significant was p < 0.0083.

Results
Behavioural results. The behavioural results are summarized in Fig. 1. Pearson's correlation analysis revealed that the online (i.e. off-task frequency; M ± SD = 3.38 ± 3.79) and retrospective measures of off-task experience (i.e. NYC-Q; M ± SD = 3.09 ± 1.37) were positively correlated (r = 0.51, p < 0.001). There was also a significant negative correlation between off-task frequency and text-based memory scores (M ± SD = 7.96 ± 2.95; r = −0.26, p = 0.029), suggesting that frequent off-task thought interferes with reading, in line with previous findings 1, 3 . However, the retrospective mind-wandering measure was not significantly associated with text-based memory (r = −0.17, p = 0.16). Experiment 1: sentence-reading task. A whole-brain analysis indicated that activation in middle/inferior temporal gyrus and temporal fusiform cortex in the contrast of Meaningless > Baseline was modulated by www.nature.com/scientificreports www.nature.com/scientificreports/ individual differences in text-based memory, measured outside the scanner (p = 0.0111; see Fig. 2A). To understand the nature of this relationship, we plotted the relationship between mean % signal change in this region and text-based memory across individuals. People with better text-based memory showed a stronger response to orthographic input, even when this was not meaningful.
We did not find any clusters for the Meaningful > Meaningless contrast that varied with individual differences in either text-based memory or off-task thought. To identify whether the association with text-based memory in Meaningless > Baseline showed a similar pattern to Meaningful > Baseline, we used the cluster identified in the contrast of Meaningless > Baseline as a mask to extract the mean % signal change in the contrast of Meaningful > Baseline for each individual, then performed Bayesian Pearson Correlation Inference analysis to quantify the relationship between the text-based memory score and activation in the Meaningful > Baseline contrast. The estimated Pearson correlation coefficient was 0.30, p = 0.012, and the corresponding Bayes Factor was 0.46. This score is a natural ratio comparing the likelihood of no correlation with the likelihood of a correlation between text-based memory and brain activation for the Meaningful > Baseline contrast. Since the Bayes Factor score was less than 1, this suggests that there is 'moderate evidence' that the association is present in both conditions. Consequently, the effect of text-based memory on the BOLD response is most likely to reflect a generally greater response to orthographic input in participants with better text-based memory.
We compared the location of this temporal lobe cluster that reflected a stronger BOLD response to orthographic inputs in participants with good text-based memory (shown in red in Fig. 2B) with the main effect of meaning across the group in Experiment 1. The location of this temporal lobe, which showed an effect of individual differences, was adjacent on the cortical surface to the mean task activation in the contrast of Meaningful > Meaningless (shown in blue in Fig. 2B). This temporal lobe activation is implicated in visual-to-semantic processes in contemporary accounts that propose graded abstraction from unimodal visual to heteromodal conceptual representations within the temporal lobe 5 .
The cluster that showed a stronger response to orthographic input in people with text-based memory was located at the intersection of DMN, FPN and DAN (see Fig. 2A). We calculated the percentage of voxels within this cluster that fell within the large-scale networks defined by Yeo, et al. 61 . Of these voxels, 48.1% were within the DMN (overlap in pink), which is implicated in both reading comprehension and spontaneous thoughts 46,47,58 ; 17.4% fell within the adjacent FPN (overlap in yellow), which plays a central role in cognitive control 59 ; and 24.8% fell within the DAN (overlap in white), which supports externally directed attention 60 . There were smaller overlaps with limbic and visual networks, 8% and 1.8% respectively, which we do not discuss further. Experiment 2: Resting-state functional connectivity. Since the region that showed greater responsiveness to orthographic input in people with good text-based memory fell at the intersection of three large-scale networks (DMN, FPN, DAN), we conducted a second experiment to understand whether the organisation of one of these large-scale networks also relates to individual differences in off-task thinking and reading. We therefore identified voxels that showed greater activation to orthographic input for people with good text-based memory in Experiment 1, which also fell within DMN, FPN, and DAN -implicated respectively in memory, control, and attention processes according to a commonly used whole-brain parcellation 61 . We used these DMN, FPN, and DAN regions as seeds for an analysis of intrinsic connectivity in Experiment 2 (see Fig. 2C).
Group-level intrinsic connectivity maps for the DMN, FPN, and DAN seed regions (i.e., irrespective of performance) are presented in Fig. 3. To understand how the regions of positive and negative connectivity from these seed regions respectively correspond to the networks implicated in semantic, cognitive control, and attention processing, we compared these spatial maps to the meta-analytic maps generated for the terms semantic, cognitive control, and attention using Neurosynth 63 . This revealed that regions of relatively high connectivity from DMN, FPN, and DAN seed regions respectively (shown in red in Fig. 3) largely overlapped with regions important for these aspects of cognition according to task-based fMRI (shown in green, with the overlap in yellow).

Relationship to individual differences. We explored whether individual differences in performance on
the factual questions about the text and off-task thought were associated with variation in patterns of intrinsic connectivity from these seeds. We generated functional connectivity maps for each region, for each individual, and then analysed these spatial maps using a series of multiple regression analyses that included individual scores in off-task thought (i.e., off-task frequency and NYC-Q) and text-based memory as explanatory variables. There were no significant differences in the connectivity of the FPN seed region in ITG that related to either off-task thought or text-based memory, so this site is not discussed further. There were some effects for the DAN seed in ITG/LOC but these failed to survive Bonferroni correction for the number of seeds and the two-tailed nature of our tests (see Section 3.3.3.2.).
Performance on factual questions related to the text. We found that MTG connectivity was related to memory for the content of the text. Participants with better text-based memory scores showed stronger connectivity between the MTG DMN seed region and anterior cingulate cortex (cingulate gyrus and paracingulate gyrus; uncorrected p = 0.006). This cluster is illustrated in Fig. 4. Of the voxels within the anterior cingulate cluster that fell within the large-scale networks defined by Yeo, et al. 61 , 88.3% were within DMN, 11.3% fell within FPN, and 0.4% fell within ventral attention network. These findings show that connectivity between different nodes of DMN is linked to better text-based memory (not poorer performance, as a task-negative theory of DMN might predict).
Off-task Frequency. We also found that increasing off-task frequency was associated with weaker connectivity between MTG in DMN and visual cortex (intracalcarine cortex, precuneus cortex, and lingual gyrus; uncorrected p = 0.006). This effect is presented in Fig. 4. Of the voxels in this cluster that fell within one of the large-scale www.nature.com/scientificreports www.nature.com/scientificreports/ networks defined by Yeo, et al. 61 , 100% were within the visual network. Consequently, participants with stronger intrinsic connectivity at rest between DMN and visual cortex were less likely to engage in off-task thinking while reading.
Additional effects. There were several additional effects that were significant at the whole-brain level that did not survive Bonferroni correction for the number of models and the two-tailed nature of our tests (e.g., p > 0.0083). When considering these results, it is important to note that these may reflect Type II errors and should be considered accordingly. Overlap of DMN seed connectivity maps with semantic meta-analytic map. The group-level patterns of relatively high (in red) and low (in blue) functional connectivity from the DMN seed in MTG during resting-state fMRI (cluster correction, z > 2.6, p < 0.05), and the overlap of these positive and negative networks with a semantic meta-analytic map (regions in green) derived from Neurosynth (using 'semantic' as a search term). (B) Overlap of FPN seed connectivity maps with cognitive control meta-analytic map. The group-level patterns of relatively high (in red) and low (in blue) functional connectivity from the FPN seed in ITG during resting-state fMRI (cluster correction, z > 2.6, p < 0.05), and the overlap of these positive and negative networks with a cognitive control meta-analytic map (regions in green) derived from Neurosynth (using 'cognitive control' as a search term). (C) Overlap of DAN seed connectivity maps with attention meta-analytic map. The group-level patterns of relatively high (in red) and low (in blue) functional connectivity from the DAN seed in ITG/LOC during resting-state fMRI (cluster correction, z > 2.6, p < 0.05), and the overlap of these positive and negative networks with attention meta-analytic map (regions in green) derived from Neurosynth (using   www.nature.com/scientificreports www.nature.com/scientificreports/ within the large-scale networks defined by Yeo, et al. 61 , 69.5% were within the default mode network, 16.9% fell within the frontoparietal network, and 13.6% fell within the ventral attention network. (iii) Higher NYC-Q scores were linked to greater connectivity between MTG and parahippocampal gyrus as well as temporal fusiform cortex (uncorrected p = 0.028). Of the voxels within parahippocampal gyrus that fell within the large-scale networks defined by Yeo, et al. 61 , 96.6% were within the visual network and 3.4% fell within the default mode network.
Seeding from ITG/LOC DAN region: Analysis of the functional connectivity of DAN in ITG/LOC showed that poor text-based memory was linked to greater connectivity between the left ITG and (i) the right lateral occipital cortex (uncorrected p = 0.041; see Fig. 6) as well as (ii) the right inferior and middle temporal gyrus (uncorrected p = 0.015). For the voxels within the right lateral occipital cortex that fell within the large-scale networks defined by Yeo, et al. 61 , 56.8% fell within the dorsal attention network, 28.4% were within visual network, 5.8% fell within default mode network, and 2.3% fell within the frontoparietal network. For the voxels within the right inferior/middle temporal gyrus that also fell within the large-scale networks defined by Yeo, et al. 61 , 69.8% fell within the frontoparietal network, 29.3% were within dorsal attention network, and 0.8% fell within the default mode network.

Summary of results.
Our study set out to examine the neural mechanisms that contribute to reading performance, assessed using factual questions answered from memory about the content of a text, and periods of off-task thinking during reading. Using a data-driven approach, Experiment 1 identified regions within the middle temporal gyrus that activated more for people with better text-based memory in the sentence-reading task. This region fell at the intersection of three large-scale networks -the DAN, FPN and DMN. In Experiment 2, we separated the MTG cluster into sub-regions that corresponded to each of these networks and used them in an analysis of resting-state connectivity to establish whether the intrinsic architecture of any of these regions was predictive of variation in either patterns of off-task thought or text-based memory. We found no evidence that variation in either memory for the text or off-task thought was linked to the connectivity of the FPN region. Weak evidence emerged for a role of the DAN in reading: weaker connectivity with regions of both the right angular gyrus/lateral occipital cortex and right middle temporal gyrus was linked to better text-based memory, although this result did not survive correction for the number of seeds and the two-tailed nature of our tests. The strongest evidence was observed for the DMN since the MTG sub-region showed patterns of connectivity that were linked to both reading and off-task thinking. The most robust of these effects were (i) stronger connectivity with a medial prefrontal region, also in DMN, that was associated with better text-based memory, and (ii) weaker connectivity with visual cortex, linked to greater frequency of off-task thought. Together these results provide converging www.nature.com/scientificreports www.nature.com/scientificreports/ support for the role of multiple large-scale systems in the ability to read for meaning, and specific support for the importance of the DMN in both memory for texts, as well as for the occurrence of off-task thought during reading.

General Discussion
We show how a region of the lateral temporal cortex within the default mode network (DMN) is implicated in both on-task processing (i.e. text processing) and off-task mental states (such as periods of off-task thought). In Experiment 1, which employed a sentence-reading task in the scanner, we found people who were better able to answer questions about a text that they had read previously showed a greater BOLD response to orthographic inputs in middle temporal gyrus (MTG) within DMN, and a region of inferior temporal gyrus (ITG) in FPN, as well as another region of ITG/lateral occipital cortex in DAN. In Experiment 2, we explored individual differences in the intrinsic connectivity of these sites. For individuals with more frequent off-task thought, MTG within DMN showed weaker connectivity with visual cortex, suggesting that perceptual decoupling may promote off-task thought. In contrast, for individuals with better text-based memory, MTG showed greater connectivity with anterior cingulate cortex, also in the DMN. These findings show that DMN regions in lateral temporal cortex have patterns of connectivity that uniquely support both reading, and states that are detrimental to making sense of what one is reading (i.e. off-task thought).
Although our results show relatively clear evidence for the involvement of the DMN in both reading and off-task thought, our study failed to find robust evidence supporting a role of the DAN or the FPN. No whole-brain results were observed for the FPN. For the DAN, better text-based memory was associated with weaker correlation between regions of the right temporal and parietal cortex that fell largely within the FPN and DAN. Although it may seem paradoxical that higher connectivity within the DAN is linked to poorer reading performance, our prior studies using a sustained attention task found lower connectivity within this system (and in particular to the same LOC cluster) was linked to a greater tendency for attention to remain coupled to the task 43 . Moving forward, our studies suggest that it will be important to assess the neural basis of different features of experience across multiple task contexts.
Our results add to a growing body of evidence that DMN plays a complex role in reading. Our prior study found that DMN connectivity to different regions was linked to both better and poorer comprehension. The current study shows that the MTG region may be important in the capacity of the DMN to contribute to apparently opposing mental states. Our study suggests that rather than different temporal lobe regions supporting on-task and off-task semantic retrieval (for example, regions falling within FPN and DMN respectively), diverse patterns of connectivity from the same DMN region can underpin both off-task thought and reading. It has already been observed that semantic regions within temporal cortex have a pattern of connectivity to both DMN core and visual cortex 19,64,65 -our results suggest that both of these connections are important for good text-based memory. Our results are also broadly consistent with the recent observation that while DMN often shows a response to nonwords over words (reflecting off-task processing), these DMN regions also support semantic processes engaged in reading 66 .
Functionally, MTG is implicated in heteromodal aspects of cognition as the inputs along auditory and visual processing streams maximally converge here 5,19,64,67 . MTG responds more strongly to memory-based and meaning-based decisions, consistent with the location of this cluster at the anterior end of the ventral visual stream within the DMN 68 . In line with these studies, the anterior and middle temporal lobe have been identified as important for text comprehension 9,69,70 . Nevertheless, activation within MTG is insufficient for comprehension -our results suggest this region also needs to be strongly activated by visual inputs and to interact with other regions of DMN implicated in comprehension.
Finally, our study also provides evidence that poor reading is linked to inattention or perceptual disengagement 38,71 . First, we found that poor memory for texts was linked to lower levels of activity in MTG in response to orthographic inputs. Since this response was identified in the meaningless condition, it is likely to be a consequence of how perceptual input rather than meaning is processed. Second, we found that the aspect of this region that fell within the DMN was less coupled to primary visual cortex for individuals who were frequently off-task while reading. Event-related potentials evoked by sensory inputs are reduced in magnitude during episodes of off-task thought, relative to on-task periods 40 . The posterior core of the DMN which supports heteromodal integration 72 also contributes to different types of spontaneous thought 47 . The role of MTG in processing texts and in off-task thought may be similar: perceptual decoupling of MTG from visual cortex may allow this region to support off-task thought that is unrelated to the immediate external environment. This pattern is generally consistent with the cascade model of inattention 71 , which argues that off-task states during reading partly reflect reductions in perceptual processing. This leads to cascading consequences affecting both comprehension and memory for texts 1 .
While DMN regions support off-task states that impair text processing, connectivity within DMN predicted good memory for texts. In people who were able to answer more factual questions about what they had read, MTG coupled more with anterior cingulate cortex, and both regions fell largely within DMN as defined by Yeo, et al. 61 . Both of these regions are 'hubs' that integrate diverse elements of cognition 64,67,73,74 . Anterior cingulate cortex shows graded connections at rest with both sensory and motor cortices, as well as with memory/DMN regions 75 . Extrapolating from our results, individuals who had the most efficient reading experience (i.e. who experienced less off-task thought and answered more questions about the text) would show a combination of strong within-DMN connectivity and less decoupling with the visual system. Although there are likely to be functional subdivisions within DMN, findings from our study directly concerned with attentional lapses are hard to accommodate within the commonly held view of the DMN as supporting off-task states [76][77][78] . Instead our study adds to a growing body of evidence that this system can make important contributions to external task processing 68,79-83 .
In conclusion, we found that dissociable patterns of activation and intrinsic connectivity in an MTG region within DMN predicted text-based memory and off-task thought. Better performance on questions about the text was associated with greater coupling of MTG with another DMN region in anterior cingulate gyrus. In contrast, greater disconnection between MTG and primary visual cortex was associated with frequent off-task thought. We conclude that DMN regions in lateral temporal cortex not only help us to process information in the external environment, but also form thoughts that can be independent from what is happening around us -however, both of these aspects of cognition are supported by a broader network of brain regions.