Functional connectivity of the sensory system and executive functions during a story listening task is related to parent-child interaction during joint reading: a functional MRI-diffusion map study

Functional connectivity of the sensory system and executive functions during a story listening task is related to parent-child interaction during joint reading: a functional MRI-diffusion map study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Functional connectivity of the sensory system and executive functions during a story listening task is related to parent-child interaction during joint reading: a functional MRI-diffusion map study Tzipi Horowitz Kraus, Adi Jacobson, John Hutton, Tzipi Horowitz-Kraus This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5003291/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Brain Imaging and Behavior → Version 1 posted 12 You are reading this latest preprint version Abstract The quality of parent-child interaction during shared reading may influence the activation and synchronization of reading-related brain networks. But could differences in brain activity while a child is listening to stories predict parent-child interaction level during reading? For this study, functional MRI including a stories listening task was performed with 22 4-year-old girls and behavioral measurement scores reflecting parent-child interaction as well as maternal depression levels affecting these interactions were collected using video observation data of a shared reading task of these children with their mothers. The study aim was to apply the fMRI stories-listening data to create a diffusion maps algorithm and then attempt to classify the level of parent-child interaction during a shared reading task outside of the scanner. The diffusion maps algorithm successfully clustered children in this manner, with higher parent-child engagement scores related to diffusion patterns in regions of the brain known to support reading. This study demonstrates that applying this diffusion maps algorithm to brain functional connectivity data can predict parent-child interaction during shared book reading. This algorithmic approach is a potential, novel, data-driven means to quantify parent-child interaction in different contexts (e.g., reading, play) and populations. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Neurobiological correlates for stories listening Reading, the ability to identify words and understand their meaning, is a crucial skill in modern life. However, since this skill is evolutionary new, brain areas that evolved to support more basic functions (e.g., language, vision) must be stimulated and then integrated, ideally during early childhood when the brain is highly plastic, to achieve reading proficiency[ 1 ]. This synchronization process has been termed “emergent literacy” and typically begins in infancy and extends through school age[ 2 ]. Brain regions known to support emergent literacy and reading have been well described and include the superior temporal gyrus and the angular gyrus (linguistic abilities) [ 3 ], frontal regions (executive functions, comprehension) [ 2 ] [ 4 ], and visual areas including the fusiform gyrus (letter and word recognition) [ 5 ]. Interestingly, the same neural circuits supporting reading abilities are also involved in pre-reading activities, such as during stories listening [ 6 ]. More nurturing home literacy environment has been linked to stronger activation of visual processing-related regions and executive functions during stories listening in 3–5 years old children, using magnetic resonance imaging (MRI) [ 7 ]. Activation of these regions has been attributed to imagination and comprehension, and likely helps shape the future reading network and abilities [ 6 , 8 ]. It has also been suggested that stories listening in an engaged manner (i.e. interactive “shared” reading) engages similar brain regions in young children at a pre-reading age [ 1 ]. These studies suggest that story listening supports emergent literacy and subsequent reading skills by stimulating and even synchronizing brain regions and networks at this formative age[ 6 ]. Stories listening: an important facilitator for parent-child interaction Brain development is affected by a child’s interactions with their environment beginning in infancy [ 9 ]. Although children have many types of interactions with their environment, the most meaningful is with their parents [ 1 ]. Behavioral and neurobiological evidence claims that shared book reading, especially involving a child and their parent, is beneficial for developing and synchronizing brain regions supporting reading and learning [ 7 ]. It has been shown that high levels of parent-child interaction during this activity may be especially impactful on neurobiological and cognitive levels, leading to an improvement in reading abilities and outcomes [ 1 ]. An evidence-based construct for interactive reading has been termed dialogic reading, which involves specific types of prompts and responses during a story [ 10 ]. Higher levels of interactivity (dialogic-ness) has been linked to improved language and cognitive abilities in children [ 1 ]. This has been recently supported by MRI studies showing associations between interactivity during storytelling and the engagement of neural circuits supporting language and executive functions [ 1 ]. Other studies examining the effect of dialogic, interactive reading with children at that age, echoed these findings using EEG measures[ 11 ]. Maternal depression and storytelling Maternal depression is characterized by low mood, loss of interest and reduced activity, which has negative impacts on parenting and child adjustment [ 12 ]. It has been shown that mothers with maternal depression tend to read less to their children [ 13 ]. Additionally, Mothers with depression tend to read less interactively, with fewer facial expressions and high tones that trigger a child's attention [ 8 ]. Maternal depression is related to a range of adverse child health outcomes, including lower linguistic abilities[ 8 ]. Impacts on neurobiological measures have also been described, as in lower functional connectivity in brain regions related to executive functions and visual processing during stories listening in 4 year-olds with depressed mothers [ 8 ]. This was attributed to lower stimulation during storytelling and consequently lower engagement of functional connections between these brain regions needed to imagine and attend to the stories. Although it is well known maternal depression has a negative effect on the interaction with the child and linguistic outcomes, it is not clear whether children of mothers with maternal depression share differences in brain organization related to future reading readiness. One approach that can be used to test this is by clustering characteristics of individuals using MRI diffusion maps. Diffusion maps as a method of data clustering Diffusion maps (DM) are a method for finding meaningful geometric descriptions of data sets based on diffusion processes [ 14 ]. This method changes the representation of data sets with many variables into a low-dimensional description, while preserving quantities of interest, in turn enabling classification of the data sets in a more clear and representative manner [ 15 ]. This method is based on the use of eigenfunctions of Markov matrices that can construct coordinates called diffusion maps in which the data could be represented. As the two points on the diffusion map coordinates are closer, the relationship between those data points is stronger. Moreover, the relationship between sets of data can be represented as a geometric structure on the diffusion map coordinates [ 15 ]. The goal of the current study is to determine whether parent-child interaction during an observed shared book reading session could be reflected in the child’s neural functional connectivity during a story listening fMRI task, while focusing on networks known to be involved with narrative processing. The approach was to determine the distance in brain functional connectivity matrices during shared story reading for maternal-child dyads with high vs low levels of interaction. As mothers with depression tend to interact less with their children[ 16 ], a secondary aim was to determine if high vs low levels of interaction during this book reading session would also be reflected in maternal depression levels. We hypothesized that: 1)applying the DM method to fMRI data from a stories-listening task involving children of mothers with high vs low levels of depression would successfully cluster functional connectivity networks. The selected networks in the current study are known to support reading based on the level of parent-child interaction during a shared reading task; 2) dyads with more interactive reading and also lower maternal depression levels would show greater functional connections within and between reading-related networks and that these networks would successfully cluster children based on parent-child interaction. Methods Participants The study involved 22 mother-child dyads, recruited from a longitudinal home injury prevention trial serving mothers of low-socioeconomic status (Cincinnati Home Injury Prevention (CHIP) trial). Participating children were girls between 3.0 and 4.5 years (mean age: 4.1 years, SD = 0.2) who met the following criteria: native English speakers from monolingual households, right-handed, full-term gestation, no history of head trauma with loss of consciousness or stimulant use, and no standard contraindications to MRI. All parents provided written consents. The study was approved by the Cincinnati Children’s Institutional Review Board (IRB) in accordance with the Declaration of Helsinki. Behavioral measurements Two methods were used to measures parent-child interaction level during storytelling as well as offline (i.e. Without directly measuring parent-child interaction): Parent-child interaction during storytelling (online interaction measurement) : Behavioral measurements of the mother and the mother-child interaction were collected during a shared reading task (also reported in [ 1 ]). To measure the quality of interaction, the child's engagement during the shared reading was scored by a scale that was developed and reviewed by experts [ 1 ] as follows: 0 – not engaged, 1 – somewhat engaged, 2 – very engaged, and 3 – extremely engaged. Children's engagement scores were calculated as the mean score from the 3 experts' scores. To determine the level of interaction challenges (per [ 17 ]), number of times when mothers checked their phones during the shared reading task was measured, and was scored as 1 (i.e. checked the phone) or 0 (did not check the phone), respectively (see also [ 18 ] for the scoring method). Maternal depression (an offline measure of parent-child interaction) : Maternal depression levels were assessed using the validated Beck Depression Inventory-Ⅱ (BDI-Ⅱ) [ 19 ]. Behavioral data analysis Independent t -tests were conducted for each behavioral measure and medians were calculated. Moreover, to determine the relations between parent-child interaction measures while listening to stories and the offline parent-child interaction levels, Pearson correlations between maternal depression measures and behavioral measures were conducted. Neuroimaging measurements The MRI scans were acquired in Cincinnati Children’s Hospital Medical Center, Ohio using a 3T Phillips Achieva MRI system. For fMRI, a time series of 165 blood-oxygen-level-dependent (BOLD) weighted scans covering the entire brain with 38 slices in the axial plane were continuously acquired with voxel size 3.75x3.75x5 mm at 2-second intervals (TR = 2) during the story listening task. In addition, a 3D anatomical (T1) brain image was acquired for the co-registration to the functional scans. All children were awake and non-sedated during the scan. Neuroimaging task: stories listening The stories’ listening task was composed of 11 alternating blocks of control and active condition (6 and 5 each, respectively), with a duration of 30 seconds for each block. The active condition was listening to stories which was presented by a female voice and included 9,10 or 11 sentences with varying syntactic structures and vocabulary which was matched for the preschool level (see [ 20 ] for the stories transcript and description). For the control condition, a backward speech condition was used with voices with a range of frequencies of 200–400 Hz. Neuroimaging data analysis Functional MRI data and the 3D anatomical image of each child were pre-processed using the CONN toolbox ( http://www.nitrc.org/projects/conn/ ; [ 21 ]). These steps included realigning and unwarping the data; slice-timing correction, coregistration, segmentation, normalization and smoothing. We used the Power’s atlas [ 22 ] to divide the brain to different regions of interest (ROIs) for calculating at each one the changes in BOLD over time during the stories listening task. Due to a high motion level of several children (threshold of 80% invalid volumes after the pre-processing step) only 17 functional scans were included in the analysis. Using the processed data of the functional MRI scans, we conducted a functional connectivity analysis. For each child, a connectivity matrix was created, containing the correlation of the changes in BOLD over time (during the stories listening task) between all the different ROIs as divided by the Power atlas. Since this atlas divides the brain into 264 ROIs [ 22 ], the connectivity matrix of each child was created as a 2D matrix with a size of 264×264. Diffusion Maps analysis Following a previously published method[ 23 ], connectivity matrices either for the whole brain or for selected networks supporting storytelling (i.e. visual processing, auditory processing and executive functions networks, i.e. Cingulo-opercular and fronto-parietal, see more in the next sections) were turned into a united three-dimensional connectivity matrix with a size of 17×264×264, which served as the input for the DM algorithm. This algorithm enabled lowering the high-dimension data of each child (connectivity matrix with a size of 264×264) and representing it as a single data point on a set of 3D diffusion map coordinates. For each behavioral measurement (i.e. online assessment of parent-child interaction during storytelling and offline measure for parent -child interaction), the median was calculated, and the scores were divided into two groups – above the median and under/equal to the median. Then, each data point was colored according to values greater than or lower than the median values of a chosen behavioral measurement. For each of the two groups, the centroid on the DM coordinates was calculated. Then, the distance between the two centroids was calculated and was mathematically characterized to allow a separation of the group by the behavioral measurement. To determine whether the DM better cluster the groups based on whole-brain ROIs or based on the selected networks associated with storytelling, both approaches were tested, including a whole brain analysis and network-based analysis with the following combinations: 1) visual, auditory, and EF networks (cingulo-opercular, fronto-parietal); 2) visual, and EF networks (cingulo-opercular, fronto-parietal); and 3) auditory, and EF networks (cingulo-opercular, fronto-parietal). These networks were previously uses in our studies[ 24 , 25 ], and the ROIs were defined in [ 22 ]. Correlations between the distance of the points from the centroids and behavioral measurements After applying the between-networks connectivity information to the DM algorithm, the distance between the centroids of each group (above the median or less/equal than the median) was calculated applying each behavioral measure (parent-child interaction during storytelling, maternal depression). Results Behavioral measures Demographic characteristics for the study’s sample are described in Table 1. ---Insert Table 1 about here--- Table 1. Demographic characteristics N % Gender Female 17 100 Annual household income ($) Under 5,000 7 41 5,000-10,000 4 23 10,000-30,000 3 18 30,000-50,000 3 18 Maternal Education level High school graduate or less 8 47 Some college 8 47 College graduate 1 6 Characterizing the study population by parent-child interaction online and offline Parent-child interaction (online) median calculation: Of the 17 children who participated in the both the MRI session and in the shared reading task with their mothers, eight had engagement scores less than the median (1.67) and one child got a score that was equivalent to the median (a total of nine children who had under/equal the median score), whereas eight had greater scores for their engagement in the shared reading task. Interaction challenges were also assessed, whereases nine mothers checked their phones and eight mothers did not. See Table 2. Parent-child interaction (offline) median calculation: Of the 17 mothers, seven demonstrated BDI scores less than the median (median: 8.67) and two had BDI scores which are equivalent to the median (a total of nine mothers had under/equal the median scores) whereas eight mothers had BDI scores greater than the median. See Table 2. ----Insert Table 2 about here--- Table 2. Behavioral measurements of the mothers and the parent-child interaction during the shared reading task Mean (SD) Median Min-max Number of mothers below or equal the median Number of mothers above median Child engagement level (online measure) 1.57 (0.97) 1.67 0-3 9 8 Engagement challenges (online measure) 0.47 (0.51) 0 0-1 9 8 Maternal depression; BDI (offline measure) 12.73 (7.98) 8.67 2.67-29.27 9 8 Table 2. The engagement challenges were assessed using the CONNECT measure (see [18]) focusing on the number of times the parent checked their phones during the storytelling activity. Pearson correlation between parent-child interaction during storytelling measures and offline parent-child interaction measures A positive trend was found between maternal depression levels and the online measure for parent-child interaction challenges (i.e the number of phone checks by the mother) (r=.389, p=.06). Additionally, a negative trend between maternal depression and storytelling interaction (r=-.363, p=.07). A Pearson correlation within the parent-child online interaction measures during storytelling revealed a negative correlation between the number of phone checks and the level of child engagement (r=-0.778, p<0.001) indicating more phone checks were related to lower interaction during storytelling. Results suggest that higher maternal depression levels are related to more phone checks and lower interaction with the child during storytelling and that more phone checks are related to lower child engagement. Neuroimaging results Whole brain analysis The whole brain matrices included in the DM algorithm, demonstrated a successful classification of the participants by the “Child Engagement” and “Phone Check” online behavioral measurements as the input. The DM algorithm also successfully clustered the participants by their maternal depression level (BDI measurement). See Figure 1. ----Insert Figure 1 about here--- Figure 1. Whole brain analysis during story listening. The black color represents datasets with values lower than the median; red color: values greater than the median. The green color represents the centroid for datasets with values lower than the median and the blue color represents centroids for datasets with values greater than the median. The upper maps represent the data for the division by parent-child interaction during storytelling (left; child engagement, right; number of times the mother checked the phone). The lower map represents the data for the division by offline mother-child interaction (i.e. maternal depression). DM containing the combination of the Visual, FP, and CO networks Importing into the DM the specific functional networks for the ROIs in the selected visual processing and EF networks (FP, CO) during the stories-listening task, and coloring them based on the median values above and below the three selected measurements, the DM algorithm was able to split the full cohort into the two groups of high vs low interaction (i.e. successfully classified) the participants by the “Child Engagement” and “Phone Check” online behavioral measurements. See Figure 2. ----Insert Figure 2 about here--- Figure 2. Visual processing and EF networks-based DMs. The black color represents datasets with values lower than the median; red color: values greater than the median; green color; centroid for datasets with values lower than the median and the blue color represents centroids for datasets with values greater than the median. The upper maps represent the data for the division by parent-child interaction during storytelling (left; child engagement, right; number of times the mother checked the phone). The lower map represents the data for the division by offline mother-child interaction (i.e. maternal depression). DM containing the combination of the Auditory, FP, and CO networks Importing into the DM the functional connectivity networks from the auditory processing and EF networks (FP, CO) during the stories-listening task, and coloring them based on the median values above and below the three selected measurements, the DM algorithm successfully classified the participants only by the “Child Engagement” behavioral measurement and was not sensitive for the differences between the groups while focusing on the number of phone checks. See Figure 3. ----Insert Figure 3 about here--- Figure 3. Auditory processing and EF networks-based DMs. The black color represents datasets with values lower than the median; red color: values greater than the median; green color; centroid for datasets with values lower than the median and the blue color represents centroids for datasets with values greater than the median. The upper maps represent the data for the division by parent-child interaction during storytelling (left; child engagement, right; number of times the mother checked the phone). The lower map represents the data for the division by offline mother-child interaction (i.e. maternal depression). DM containing the combination of the Auditory, Visual, FP, and CO networks Importing into the DM the functional connectivity networks from the auditory and visual processing and EF networks (FP, CO) during the stories-listening task, and coloring them based on the median values above and below the three selected measurements did not show successful clustering. See Figure 4. ----Insert Figure 4 about here--- Figure 4. Visual and auditory processing and EF networks-based DMs. The black color represents datasets with values lower than the median; red color: values greater than the median; green color; centroid for datasets with values lower than the median and the blue color represents centroids for datasets with values greater than the median. The upper maps represent the data for the division by parent-child interaction during storytelling (left; child engagement, right; number of times the mother checked the phone). The lower map represents the data for the division by offline mother-child interaction (i.e. maternal depression). Correlations between the distance from the centroids of each group, per DM condition and behavioral measures Table 3 demonstrates the distances between centroids of each group (low vs high parent-child interaction), for each networks’ combination and parent-child interaction condition. The largest distance between centroids were found for the combination of the visual, CO, and FP networks, by the parent-child interaction measures during storytelling (i.e. “Child Engagement” and “Phone Check” measurements), which were 0.3067 and 0.3187, respectively. These results indicate that the DM algorithm best classified the neural matrices by these behavioral measurements, into two different clusters. The distances between the centroids, separating the neural data by the offline parent-child interaction (BDI measurement), were nearly close, for all the between-networks analyses, which indicates that the DM algorithm could not cluster more successfully between the different between-networks analyses. ----Insert Table 3 about here--- Table 3. Distances between the centroid Whole brain Visual-EF Auditory-EF Visual-Auditory-EF Offline Online Offline Online Offline Online Offline Online BDI Child Engagement Phone Check BDI Child Engagement Phone Check BDI Child Engagement Phone Check BDI Child Engagement Phone Check Distance between centroids 0.206 0.185 0.264 0.196 0.306 0.318 0.168 0.188 0.279 0.192 0.158 0.214 Table 3. The distances between centroids for each parent-child interaction measure (o line and offline) for each networks combinations is presented in the table. Discussion This study aimed to determine the feasibility of DM to cluster children in terms of more vs less interactive maternal-child shared reading, based on brain connectivity patterns captured using fMRI during a stories listening task. This approach allows an objective clustering of groups with shared characteristics, without the need to down-sample the data or reduce its dimensionality. In line with our hypothesis, the DM algorithm successfully classified the children in terms of parent-child interaction level as measured using both “Child Engagement” and “Phone Check” behavioral measurements during the shared-reading task, based on the fMRI connectivity data involving ROIs of interest during the stories-listening task. The best classification (compared to a whole brain approach or other networks combinations) was obtained when importing the connectivity matrix of the visual processing and EF networks to the DM algorithm, quantified via larger distances between centroids of each group. However, contrary to our hypothesis, the DM algorithm did not successfully classify the children by maternal depression level. We attribute this to the moderate correlation between higher maternal depression level and behavioral measures (phone check, reading engagement) , which may not be sensitive enough to differentiate children of mothers with maternal depression. Additional behavioral measures might be needed for this purpose (i.e. the level of eye contact, touch, etc) The importance of sensory processing and EF in stories engaging The results of the current study are in line with previous findings highlighting the involvement of neural networks related to sensory processing and EF while listening to studies to children’s engagement in listening to the story[18, 26, 27]. These studies, as well as ours, strengthen the evidence that stories listening is not a passive process. Indeed, robust engagement in shared reading involves both imagery (i.e. visual processing) and cognitive control (i.e. EF), neural networks that are engaged via higher parental interactivity. Critical questions are raised regarding the importance of parent-child engagement during storytelling in populations that engage these networks differently than in typical populations. These include children with reading difficulties who share challenges in engaging the visual cortices[28] or children with attention difficulties who share challenges in engaging neural networks related to EF[29]. Moreover, it is unclear whether interventions such as Dialogic Reading that are intended to enhance interactivity during storytelling[11][30] also can fuel higher engagement and connectivity in these networks. Additional studies should address this question. Study’s limitations This study has several limitations that should be noted. The results are based on a relatively small sample size (n = 17), limiting statistical power. Conducting a study with more participants would possibly improve the DM algorithm's ability to cluster the fMRI data and would likely better differentiate connectivity matrices of participants. Additionally, in the current study, no strong correlation was found between the depression measure (BDI) and behavioral measures of children during the shared reading task. A future study should reassess the correlation of those measurements and determine the ability of the DM algorithm to cluster participants by this measure or others. Moreover, the main (online) measure for child engagement involved direct observation of maternal-child interaction during storytelling, while the offline measure for engagement was the BDI questionnaire, which may not be optimally aligned. Lastly, the focus of this study was clustering participants by the DM algorithm based on several between-networks information. Although the DM algorithm successfully clustered participants here by applying the neural information of the Vis-CO-FP networks, there are additional networks that may more efficiently cluster participants, which is worthy of further study. Conclusions and Future directions The current study suggests that the DM algorithm has the potential to predict the quality of parent-child interaction using fMRI connectivity data. By conducting the neural information of the Visual processing, CO and FP networks, the DM algorithm successfully clustered children by two direct behavioral measures (dialogic interactivity, phone checking) during observed shared reading with their mothers. The results suggest that the DM algorithm may be able to represent visually connections between neurological and behavioral information related to shared book reading. Additional, potential applications of this data-driven algorithmic approach could include populations of interest (e.g., behavioral conditions, poverty) and other cognitive domains. Declarations Author Contribution THK and JH secured funding; AJ- analysis, AJ, THK, JH- wrote the first draft and reviewed, THK- mentorship Data Availability Data will be available upon request from the corresponding author (THK) Funding declaration This study was supported by the Technion-CCHMC next generation grant (PI: Horowitz-Kraus, Hutton). Human Ethics and Consent to Participate declarations All parents provided written consents. The study was approved by the Cincinnati Children’s Institutional Review Board (IRB) in accordance with the Declaration of Helsinki. References J. S. Hutton et al. , "Story time turbocharger? Child engagement during shared reading and cerebellar activation and connectivity in preschool-age children listening to stories," Plos one, vol. 12, no. 5, p. e0177398, 2017. N. U. Dosenbach, D. A. Fair, A. L. Cohen, B. L. Schlaggar, and S. E. 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Horowitz-Kraus, "Greater utilization of executive functions networks when listening to stories with visual stimulation is related to lower reading abilities in children," Brain and Cognition, vol. 177, p. 106161, 2024. J. S. Hutton, T. Horowitz-Kraus, A. L. Mendelsohn, T. DeWitt, and S. K. Holland, "Home Reading Environment and Brain Activation in Preschool Children Listening to Stories," (in eng), Pediatrics, vol. 136, no. 3, pp. 466-78, Sep 2015, doi: 10.1542/peds.2015-0359. R. Farah et al. , "Maternal depression is associated with altered functional connectivity between neural circuits related to visual, auditory, and cognitive processing during stories listening in preschoolers," Behavioral and Brain Functions, vol. 16, pp. 1-12, 2020. B. L. Schlaggar and B. D. McCandliss, "Development of neural systems for reading," Annual review of neuroscience, vol. 30, pp. 475-503, 2007, doi: 10.1146/annurev.neuro.28.061604.135645. S. Cortese, "The neurobiology and genetics of Attention-Deficit/Hyperactivity Disorder (ADHD): what every clinician should know," (in eng), European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society, vol. 16, no. 5, pp. 422-33, Sep 2012, doi: 10.1016/j.ejpn.2012.01.009. A. A. Zevenbergen, & Whitehurst, G. J. , "Dialogic reading: A shared picture book reading intervention for preschoolers," On reading books to children: Parents and teachers, pp. 177-200, 2003. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Brain Imaging and Behavior → Version 1 posted Editorial decision: Revision requested 31 Oct, 2024 Reviews received at journal 11 Oct, 2024 Reviews received at journal 10 Oct, 2024 Reviews received at journal 29 Sep, 2024 Reviewers agreed at journal 18 Sep, 2024 Reviewers agreed at journal 17 Sep, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers agreed at journal 15 Sep, 2024 Reviewers invited by journal 15 Sep, 2024 Editor assigned by journal 15 Sep, 2024 Submission checks completed at journal 09 Sep, 2024 First submitted to journal 30 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Kraus","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie3RMWrDMBSA4ScEymLqVVNyhVcKLlncq8gY6iW0hS6GZnApOFvnHCNHkBEoi6CrRmfx1MFT6RRqizTQEGXuoH8QD1sfshFAKPQPu5OkAgFqnEk7LALo4RWbnCcMHNHjTFH+IdRHXMbN3JFjPhKrum3BPsbronvpy/ThdkJ1C8sUrrynNCsU0M/XdpFYafLn+RsrEHTu/zDyWvOBINgFs00ts42KEg5M+gklI7E4s0X31Ox/yf4CYY4YRCsSaKoDIfUFEpHhX1Djtfm84UbnA2H3PHvPIy+Jt93uu1Q43Ra7vlym2eZDad5/pdPZqjpv3NXg6TMBEPn2w/E2Q6FQKOTrB+zGWNIAK9P+AAAAAElFTkSuQmCC","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tzipi","middleName":"Horowitz","lastName":"Kraus","suffix":""},{"id":364971934,"identity":"f512dfa0-9398-4c7b-ad22-eabc3f274814","order_by":1,"name":"Adi Jacobson","email":"","orcid":"","institution":"Technion – Israel Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Adi","middleName":"","lastName":"Jacobson","suffix":""},{"id":364971935,"identity":"804848b0-a9ea-4919-8332-ff5df305f9a0","order_by":2,"name":"John Hutton","email":"","orcid":"","institution":"Cincinnati Children’s Hospital Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Hutton","suffix":""},{"id":364971936,"identity":"de3b9a9e-2d4c-4747-91de-43ac89bbcfd2","order_by":3,"name":"Tzipi Horowitz-Kraus","email":"","orcid":"","institution":"Technion – Israel Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tzipi","middleName":"","lastName":"Horowitz-Kraus","suffix":""}],"badges":[],"createdAt":"2024-08-30 10:42:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5003291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5003291/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11682-025-01037-2","type":"published","date":"2025-07-02T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67193185,"identity":"54cec008-35f8-4fbe-bbc9-0a10cbabfa2b","added_by":"auto","created_at":"2024-10-22 08:47:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":360384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWhole brain-based diffusion maps, colored by the medians of the parent-child interaction during the online story-listening measures (engagement with the child during storytelling and the number of phone-checks) and offline parent-child interaction measures (maternal depression).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5003291/v1/745cda3f6e8c044879a0898b.png"},{"id":67193188,"identity":"f1c33965-3bdf-4947-a293-b26c2dabaaa2","added_by":"auto","created_at":"2024-10-22 08:47:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":409787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisual processing and EF networks-based diffusion maps, colored by the medians of the parent-child interaction during online story-listening measures (number of phone-checks, engagement with the child during storytelling) and offline parent-child interaction measures (maternal depression).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5003291/v1/feffe792a8644b51932a7b89.png"},{"id":67193186,"identity":"9193a167-4680-4f2b-ab92-9ccc32ee407c","added_by":"auto","created_at":"2024-10-22 08:47:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":371674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAuditory processing and EF networks-based diffusion maps, colored by the medians of the parent-child interaction during the online story-listening measures (number of phone-checks, engagement with the child during storytelling) and offline parent-child interaction measures (maternal depression).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5003291/v1/16113242570d35eae6b6aaa5.png"},{"id":67193187,"identity":"7173603e-1b1d-4683-82da-da71185a41c4","added_by":"auto","created_at":"2024-10-22 08:47:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":478402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisual, and Auditory processing and EF networks-based diffusion maps, colored by the medians of the online parent-child interaction during story-listening measures (number of phone-checks, engagement with the child during storytelling) and offline parent-child interaction measures (maternal depression).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5003291/v1/e16d1871f554505ae2bf534a.png"},{"id":86179064,"identity":"4575733a-de51-4e0e-9759-644b489121f9","added_by":"auto","created_at":"2025-07-07 16:15:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3027355,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5003291/v1/6342c8bf-2f09-4089-be85-043b31a37ec0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional connectivity of the sensory system and executive functions during a story listening task is related to parent-child interaction during joint reading: a functional MRI-diffusion map study","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eNeurobiological correlates for stories listening\u003c/h2\u003e \u003cp\u003eReading, the ability to identify words and understand their meaning, is a crucial skill in modern life. However, since this skill is evolutionary new, brain areas that evolved to support more basic functions (e.g., language, vision) must be stimulated and then integrated, ideally during early childhood when the brain is highly plastic, to achieve reading proficiency[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This synchronization process has been termed \u0026ldquo;emergent literacy\u0026rdquo; and typically begins in infancy and extends through school age[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Brain regions known to support emergent literacy and reading have been well described and include the superior temporal gyrus and the angular gyrus (linguistic abilities) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], frontal regions (executive functions, comprehension) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and visual areas including the fusiform gyrus (letter and word recognition) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, the same neural circuits supporting reading abilities are also involved in pre-reading activities, such as during stories listening [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. More nurturing home literacy environment has been linked to stronger activation of visual processing-related regions and executive functions during stories listening in 3\u0026ndash;5 years old children, using magnetic resonance imaging (MRI) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Activation of these regions has been attributed to imagination and comprehension, and likely helps shape the future reading network and abilities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It has also been suggested that stories listening in an engaged manner (i.e. interactive \u0026ldquo;shared\u0026rdquo; reading) engages similar brain regions in young children at a pre-reading age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These studies suggest that story listening supports emergent literacy and subsequent reading skills by stimulating and even synchronizing brain regions and networks at this formative age[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStories listening: an important facilitator for parent-child interaction\u003c/h2\u003e \u003cp\u003eBrain development is affected by a child\u0026rsquo;s interactions with their environment beginning in infancy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although children have many types of interactions with their environment, the most meaningful is with their parents [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Behavioral and neurobiological evidence claims that shared book reading, especially involving a child and their parent, is beneficial for developing and synchronizing brain regions supporting reading and learning [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It has been shown that high levels of parent-child interaction during this activity may be especially impactful on neurobiological and cognitive levels, leading to an improvement in reading abilities and outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn evidence-based construct for interactive reading has been termed dialogic reading, which involves specific types of prompts and responses during a story [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Higher levels of interactivity (dialogic-ness) has been linked to improved language and cognitive abilities in children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This has been recently supported by MRI studies showing associations between interactivity during storytelling and the engagement of neural circuits supporting language and executive functions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Other studies examining the effect of dialogic, interactive reading with children at that age, echoed these findings using EEG measures[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMaternal depression and storytelling\u003c/h2\u003e \u003cp\u003eMaternal depression is characterized by low mood, loss of interest and reduced activity, which has negative impacts on parenting and child adjustment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It has been shown that mothers with maternal depression tend to read less to their children [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, Mothers with depression tend to read less interactively, with fewer facial expressions and high tones that trigger a child's attention [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Maternal depression is related to a range of adverse child health outcomes, including lower linguistic abilities[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Impacts on neurobiological measures have also been described, as in lower functional connectivity in brain regions related to executive functions and visual processing during stories listening in 4 year-olds with depressed mothers [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This was attributed to lower stimulation during storytelling and consequently lower engagement of functional connections between these brain regions needed to imagine and attend to the stories. Although it is well known maternal depression has a negative effect on the interaction with the child and linguistic outcomes, it is not clear whether children of mothers with maternal depression share differences in brain organization related to future reading readiness. One approach that can be used to test this is by clustering characteristics of individuals using MRI diffusion maps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDiffusion maps as a method of data clustering\u003c/h2\u003e \u003cp\u003eDiffusion maps (DM) are a method for finding meaningful geometric descriptions of data sets based on diffusion processes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This method changes the representation of data sets with many variables into a low-dimensional description, while preserving quantities of interest, in turn enabling classification of the data sets in a more clear and representative manner [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This method is based on the use of eigenfunctions of Markov matrices that can construct coordinates called diffusion maps in which the data could be represented. As the two points on the diffusion map coordinates are closer, the relationship between those data points is stronger. Moreover, the relationship between sets of data can be represented as a geometric structure on the diffusion map coordinates [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe goal of the current study is to determine whether parent-child interaction during an observed shared book reading session could be reflected in the child\u0026rsquo;s neural functional connectivity during a story listening fMRI task, while focusing on networks known to be involved with narrative processing. The approach was to determine the distance in brain functional connectivity matrices during shared story reading for maternal-child dyads with high vs low levels of interaction. As mothers with depression tend to interact less with their children[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], a secondary aim was to determine if high vs low levels of interaction during this book reading session would also be reflected in maternal depression levels.\u003c/p\u003e \u003cp\u003eWe hypothesized that: 1)applying the DM method to fMRI data from a stories-listening task involving children of mothers with high vs low levels of depression would successfully cluster functional connectivity networks. The selected networks in the current study are known to support reading based on the level of parent-child interaction during a shared reading task; 2) dyads with more interactive reading and also lower maternal depression levels would show greater functional connections within and between reading-related networks and that these networks would successfully cluster children based on parent-child interaction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study involved 22 mother-child dyads, recruited from a longitudinal home injury prevention trial serving mothers of low-socioeconomic status (Cincinnati Home Injury Prevention (CHIP) trial). Participating children were girls between 3.0 and 4.5 years (mean age: 4.1 years, SD\u0026thinsp;=\u0026thinsp;0.2) who met the following criteria: native English speakers from monolingual households, right-handed, full-term gestation, no history of head trauma with loss of consciousness or stimulant use, and no standard contraindications to MRI. All parents provided written consents. The study was approved by the Cincinnati Children\u0026rsquo;s Institutional Review Board (IRB) in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBehavioral measurements\u003c/h2\u003e \u003cp\u003eTwo methods were used to measures parent-child interaction level during storytelling as well as offline (i.e. Without directly measuring parent-child interaction):\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eParent-child interaction during storytelling (online interaction measurement)\u003c/span\u003e: Behavioral measurements of the mother and the mother-child interaction were collected during a shared reading task (also reported in [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]). To measure the quality of interaction, the child's engagement during the shared reading was scored by a scale that was developed and reviewed by experts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] as follows: 0 \u0026ndash; not engaged, 1 \u0026ndash; somewhat engaged, 2 \u0026ndash; very engaged, and 3 \u0026ndash; extremely engaged. Children's engagement scores were calculated as the mean score from the 3 experts' scores. To determine the level of interaction challenges (per [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]), number of times when mothers checked their phones during the shared reading task was measured, and was scored as 1 (i.e. checked the phone) or 0 (did not check the phone), respectively (see also [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] for the scoring method).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMaternal depression (an offline measure of parent-child interaction)\u003c/span\u003e: Maternal depression levels were assessed using the validated Beck Depression Inventory-Ⅱ (BDI-Ⅱ) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBehavioral data analysis\u003c/h2\u003e \u003cp\u003eIndependent \u003cem\u003et\u003c/em\u003e-tests were conducted for each behavioral measure and medians were calculated. Moreover, to determine the relations between parent-child interaction measures while listening to stories and the offline parent-child interaction levels, Pearson correlations between maternal depression measures and behavioral measures were conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eNeuroimaging measurements\u003c/h2\u003e \u003cp\u003eThe MRI scans were acquired in Cincinnati Children\u0026rsquo;s Hospital Medical Center, Ohio using a 3T Phillips Achieva MRI system. For fMRI, a time series of 165 blood-oxygen-level-dependent (BOLD) weighted scans covering the entire brain with 38 slices in the axial plane were continuously acquired with voxel size 3.75x3.75x5 mm at 2-second intervals (TR\u0026thinsp;=\u0026thinsp;2) during the story listening task. In addition, a 3D anatomical (T1) brain image was acquired for the co-registration to the functional scans. All children were awake and non-sedated during the scan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNeuroimaging task: stories listening\u003c/h2\u003e \u003cp\u003eThe stories\u0026rsquo; listening task was composed of 11 alternating blocks of control and active condition (6 and 5 each, respectively), with a duration of 30 seconds for each block. The active condition was listening to stories which was presented by a female voice and included 9,10 or 11 sentences with varying syntactic structures and vocabulary which was matched for the preschool level (see [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] for the stories transcript and description). For the control condition, a backward speech condition was used with voices with a range of frequencies of 200\u0026ndash;400 Hz.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNeuroimaging data analysis\u003c/h2\u003e \u003cp\u003eFunctional MRI data and the 3D anatomical image of each child were pre-processed using the CONN toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nitrc.org/projects/conn/\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/conn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]). These steps included realigning and unwarping the data; slice-timing correction, coregistration, segmentation, normalization and smoothing. We used the Power\u0026rsquo;s atlas [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] to divide the brain to different regions of interest (ROIs) for calculating at each one the changes in BOLD over time during the stories listening task. Due to a high motion level of several children (threshold of 80% invalid volumes after the pre-processing step) only 17 functional scans were included in the analysis. Using the processed data of the functional MRI scans, we conducted a functional connectivity analysis. For each child, a connectivity matrix was created, containing the correlation of the changes in BOLD over time (during the stories listening task) between all the different ROIs as divided by the Power atlas. Since this atlas divides the brain into 264 ROIs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the connectivity matrix of each child was created as a 2D matrix with a size of 264\u0026times;264.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDiffusion Maps analysis\u003c/h2\u003e \u003cp\u003eFollowing a previously published method[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], connectivity matrices either for the whole brain or for selected networks supporting storytelling (i.e. visual processing, auditory processing and executive functions networks, i.e. Cingulo-opercular and fronto-parietal, see more in the next sections) were turned into a united three-dimensional connectivity matrix with a size of 17\u0026times;264\u0026times;264, which served as the input for the DM algorithm. This algorithm enabled lowering the high-dimension data of each child (connectivity matrix with a size of 264\u0026times;264) and representing it as a single data point on a set of 3D diffusion map coordinates.\u003c/p\u003e \u003cp\u003eFor each behavioral measurement (i.e. online assessment of parent-child interaction during storytelling and offline measure for parent -child interaction), the median was calculated, and the scores were divided into two groups \u0026ndash; above the median and under/equal to the median. Then, each data point was colored according to values greater than or lower than the median values of a chosen behavioral measurement. For each of the two groups, the centroid on the DM coordinates was calculated. Then, the distance between the two centroids was calculated and was mathematically characterized to allow a separation of the group by the behavioral measurement.\u003c/p\u003e \u003cp\u003eTo determine whether the DM better cluster the groups based on whole-brain ROIs or based on the selected networks associated with storytelling, both approaches were tested, including a whole brain analysis and network-based analysis with the following combinations: 1) visual, auditory, and EF networks (cingulo-opercular, fronto-parietal); 2) visual, and EF networks (cingulo-opercular, fronto-parietal); and 3) auditory, and EF networks (cingulo-opercular, fronto-parietal). These networks were previously uses in our studies[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and the ROIs were defined in [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between the distance of the points from the centroids and behavioral measurements\u003c/h2\u003e \u003cp\u003eAfter applying the between-networks connectivity information to the DM algorithm, the distance between the centroids of each group (above the median or less/equal than the median) was calculated applying each behavioral measure (parent-child interaction during storytelling, maternal depression).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eBehavioral measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDemographic characteristics for the study\u0026rsquo;s sample are described in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;---Insert Table 1 about here---\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDemographic characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAnnual household income ($)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eUnder 5,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e5,000-10,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e10,000-30,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e30,000-50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMaternal Education level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHigh school graduate or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCollege graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eCharacterizing the study population by parent-child interaction online and offline \u0026nbsp;\u0026nbsp;\u003c/em\u003eParent-child interaction (online) median calculation: Of the 17 children who participated in the both the MRI session and in the shared reading task with their mothers, eight had engagement scores less than the median (1.67) and one child got a score that was equivalent to the median (a total of nine children who had under/equal the median score), whereas eight had greater scores for their engagement in the shared reading task. Interaction challenges were also assessed, whereases nine mothers checked their phones and eight mothers did not. See Table 2.\u003c/p\u003e\n\u003cp\u003eParent-child interaction (offline) median calculation: Of the 17 mothers, seven demonstrated BDI scores less than the median (median: 8.67) and two had BDI scores which are equivalent to the median (a total of nine mothers had under/equal the median scores) whereas eight mothers had BDI scores greater than the median. See Table 2.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;----Insert Table 2 about here---\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eBehavioral measurements of the mothers and the parent-child interaction during the shared reading task\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2153%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8403%;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6319%;\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4514%;\"\u003e\n \u003cp\u003eMin-max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3194%;\"\u003e\n \u003cp\u003eNumber of mothers below or equal the median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003eNumber of mothers above median\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2153%;\"\u003e\n \u003cp\u003eChild engagement level (online measure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8403%;\"\u003e\n \u003cp\u003e1.57 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6319%;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4514%;\"\u003e\n \u003cp\u003e0-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3194%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2153%;\"\u003e\n \u003cp\u003eEngagement challenges (online measure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8403%;\"\u003e\n \u003cp\u003e0.47 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6319%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4514%;\"\u003e\n \u003cp\u003e0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3194%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.2153%;\"\u003e\n \u003cp\u003eMaternal depression; BDI (offline measure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.8403%;\"\u003e\n \u003cp\u003e12.73 (7.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6319%;\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.4514%;\"\u003e\n \u003cp\u003e2.67-29.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3194%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5417%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. The engagement challenges were assessed using the CONNECT measure (see [18]) focusing on the number of times the parent checked their phones during the storytelling activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePearson correlation between parent-child interaction during storytelling measures and offline parent-child interaction measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA positive trend was found between maternal depression levels and the online measure for parent-child interaction challenges (i.e the number of phone checks by the mother) (r=.389, p=.06). Additionally, a negative trend between maternal depression and storytelling interaction (r=-.363, p=.07).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA Pearson correlation within the parent-child online interaction measures during storytelling revealed a negative correlation between the number of phone checks and the level of child engagement (r=-0.778, p\u0026lt;0.001) indicating more phone checks were related to lower interaction during storytelling. Results suggest that higher maternal depression levels are related to more phone checks and lower interaction with the child during storytelling and that more phone checks are related to lower child engagement.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNeuroimaging results\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWhole brain analysis \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe whole brain matrices included in the DM algorithm, demonstrated a successful classification of the participants by the \u0026ldquo;Child Engagement\u0026rdquo; and \u0026ldquo;Phone Check\u0026rdquo; online behavioral measurements as the input. The DM algorithm also successfully clustered the participants by their maternal depression level (BDI measurement). See Figure 1.\u003c/p\u003e\n\u003cp\u003e----Insert Figure 1 about here---\u003c/p\u003e\n\u003cp\u003eFigure 1. Whole brain analysis during story listening. The black color represents datasets with values lower than the median; red color: values greater than the median. The green color represents the centroid for datasets with values lower than the median and the blue color represents centroids for datasets with values greater than the median. The upper maps represent the data for the division by parent-child interaction during storytelling (left; child engagement, right; number of times the mother checked the phone). The lower map represents the data for the division by offline mother-child interaction (i.e. maternal depression).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDM containing the combination of the Visual, FP, and CO networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eImporting into the DM the specific functional networks for the ROIs in the selected visual processing and EF networks (FP, CO) during the stories-listening task, and coloring them based on the median values above and below the three selected measurements, the DM algorithm was able to split the full cohort into the two groups of high vs low interaction (i.e. successfully classified) the participants by the \u0026ldquo;Child Engagement\u0026rdquo; and \u0026ldquo;Phone Check\u0026rdquo; online behavioral measurements. See Figure 2.\u003c/p\u003e\n\u003cp\u003e----Insert Figure 2 about here---\u003c/p\u003e\n\u003cp\u003eFigure 2. Visual processing and EF networks-based DMs. The black color represents datasets with values lower than the median; red color: values greater than the median; green color; centroid for datasets with values lower than the median and the blue color represents centroids for datasets with values greater than the median. The upper maps represent the data for the division by parent-child interaction during storytelling (left; child engagement, right; number of times the mother checked the phone). The lower map represents the data for the division by offline mother-child interaction (i.e. maternal depression).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDM containing the combination of the Auditory, FP, and CO networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eImporting into the DM the functional connectivity networks from the auditory processing and EF networks (FP, CO) during the stories-listening task, and coloring them based on the median values above and below the three selected measurements, the DM algorithm successfully classified the participants only by the \u0026ldquo;Child Engagement\u0026rdquo; behavioral measurement and was not sensitive for the differences between the groups while focusing on the number of phone checks. See Figure 3.\u003c/p\u003e\n\u003cp\u003e----Insert Figure 3 about here---\u003c/p\u003e\n\u003cp\u003eFigure 3. Auditory processing and EF networks-based DMs. The black color represents datasets with values lower than the median; red color: values greater than the median; green color; centroid for datasets with values lower than the median and the blue color represents centroids for datasets with values greater than the median. The upper maps represent the data for the division by parent-child interaction during storytelling (left; child engagement, right; number of times the mother checked the phone). The lower map represents the data for the division by offline mother-child interaction (i.e. maternal depression).\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDM containing the combination of the Auditory, Visual, FP, and CO networks\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eImporting into the DM the functional connectivity networks from the auditory and visual processing and EF networks (FP, CO) during the stories-listening task, and coloring them based on the median values above and below the three selected measurements did not show successful clustering. See Figure 4.\u003c/p\u003e\n\u003cp\u003e----Insert Figure 4 about here---\u003c/p\u003e\n\u003cp\u003eFigure 4. Visual and auditory processing and EF networks-based DMs. The black color represents datasets with values lower than the median; red color: values greater than the median; green color; centroid for datasets with values lower than the median and the blue color represents centroids for datasets with values greater than the median. The upper maps represent the data for the division by parent-child interaction during storytelling (left; child engagement, right; number of times the mother checked the phone). The lower map represents the data for the division by offline mother-child interaction (i.e. maternal depression).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrelations between the distance from the centroids of each group, per DM condition and behavioral measures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 demonstrates the distances between centroids of each group (low vs high parent-child interaction), for each networks\u0026rsquo; combination and parent-child interaction condition. The largest distance between centroids were found for the combination of the visual, CO, and FP networks, by the parent-child interaction measures during storytelling (i.e. \u0026ldquo;Child Engagement\u0026rdquo; and \u0026ldquo;Phone Check\u0026rdquo; measurements), which were 0.3067 and 0.3187, respectively. These results indicate that the DM algorithm best classified the neural matrices by these behavioral measurements, into two different clusters. The distances between the centroids, separating the neural data by the offline parent-child interaction (BDI measurement), were nearly close, for all the between-networks analyses, which indicates that the DM algorithm could not cluster more successfully between the different between-networks analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e----Insert Table 3 about here---\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Distances between the centroid\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"688\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003eWhole brain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 180px;\"\u003e\n \u003cp\u003eVisual-EF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAuditory-EF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 154px;\"\u003e\n \u003cp\u003eVisual-Auditory-EF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eOffline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eOffline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eOffline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eOffline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eBDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eChild Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003ePhone Check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eBDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eChild Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003ePhone Check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eBDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eChild Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003ePhone Check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003eBDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eChild Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePhone Check\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eDistance between centroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. The distances between centroids for each parent-child interaction measure (o line and offline) for each networks combinations is presented in the table.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to determine the feasibility of DM to cluster children in terms of more vs less interactive maternal-child shared reading, based on brain connectivity patterns captured using fMRI during a stories listening task. This approach allows an objective clustering of groups with shared characteristics, without the need to down-sample the data or reduce its dimensionality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn line with our hypothesis, the DM algorithm successfully classified the children in terms of parent-child interaction level as measured using both \u0026ldquo;Child Engagement\u0026rdquo; and \u0026ldquo;Phone Check\u0026rdquo; behavioral measurements during the shared-reading task, based on the fMRI connectivity data involving ROIs of interest during the stories-listening task. The best classification (compared to a whole brain approach or other networks combinations) was obtained when importing the connectivity matrix of the visual processing and EF networks to the DM algorithm, quantified via larger distances between centroids of each group. However, contrary to our hypothesis, the DM algorithm did not successfully classify the children by maternal depression level. We attribute this to the moderate correlation between higher maternal depression level and behavioral measures (phone check, reading engagement) , which may not be sensitive enough to differentiate children of mothers with maternal depression. Additional behavioral measures might be needed for this purpose (i.e. the level of eye contact, touch, etc)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe importance of sensory processing and EF in stories engaging\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the current study are in line with previous findings highlighting the involvement of neural networks related to sensory processing and EF while listening to studies to children\u0026rsquo;s engagement in listening to the story[18, 26, 27]. These studies, as well as ours, strengthen the evidence that stories listening is not a passive process. Indeed, robust engagement in shared reading involves both imagery (i.e. visual processing) and cognitive control (i.e. EF), neural networks that are engaged via higher parental interactivity. Critical questions are raised regarding the importance of parent-child engagement during storytelling in populations that engage these networks differently than in typical populations. These include children with reading difficulties who share challenges in engaging the visual cortices[28]\u0026nbsp;or children with attention difficulties who share challenges in engaging neural networks related to EF[29]. Moreover, it is unclear whether interventions such as Dialogic Reading that are intended to enhance interactivity during storytelling[11][30]\u0026nbsp;also can fuel higher engagement and connectivity in these networks. Additional studies should address this question.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eStudy\u0026rsquo;s limitations\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study has several limitations that should be noted. The results are based on a relatively small sample size (n = 17), limiting statistical power. Conducting a study with more participants would possibly improve the DM algorithm\u0026apos;s ability to cluster the fMRI data and would likely better differentiate connectivity matrices of participants. Additionally, in the current study, no strong correlation was found between the depression measure (BDI) and behavioral measures of children during the shared reading task. A future study should reassess the correlation of those measurements and determine the ability of the DM algorithm to cluster participants by this measure or others. Moreover, the main (online) measure for child engagement involved direct observation of maternal-child interaction during storytelling, while the offline measure for engagement was the BDI questionnaire, which may not be optimally aligned. Lastly, the focus of this study was clustering participants by the DM algorithm based on several between-networks information. Although the DM algorithm successfully clustered participants here by applying the neural information of the Vis-CO-FP networks, there are additional networks that may more efficiently cluster participants, which is worthy of further study.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions and Future directions ","content":"\u003cp\u003eThe current study suggests that the DM algorithm has the potential to predict the quality of parent-child interaction using fMRI connectivity data. By conducting the neural information of the Visual processing, CO and FP networks, the DM algorithm successfully clustered children by two direct behavioral measures (dialogic interactivity, phone checking) during observed shared reading with their mothers. The results suggest that the DM algorithm may be able to represent visually connections between neurological and behavioral information related to shared book reading. Additional, potential applications of this data-driven algorithmic approach could include populations of interest (e.g., behavioral conditions, poverty) and other cognitive domains.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTHK and JH secured funding; AJ- analysis, AJ, THK, JH- wrote the first draft and reviewed, THK- mentorship\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be available upon request from the corresponding author (THK)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Technion-CCHMC next generation grant (PI: Horowitz-Kraus, Hutton).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll parents provided written consents. The study was approved by the Cincinnati Children\u0026rsquo;s Institutional Review Board (IRB) in accordance with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ. S. Hutton\u003cem\u003e et al.\u003c/em\u003e, \u0026quot;Story time turbocharger? Child engagement during shared reading and cerebellar activation and connectivity in preschool-age children listening to stories,\u0026quot; \u003cem\u003ePlos one, \u003c/em\u003evol. 12, no. 5, p. e0177398, 2017.\u003c/li\u003e\n\u003cli\u003eN. U. Dosenbach, D. A. Fair, A. L. Cohen, B. L. Schlaggar, and S. E. Petersen, \u0026quot;A dual-networks architecture of top-down control,\u0026quot; \u003cem\u003eTrends in cognitive sciences, \u003c/em\u003evol. 12, no. 3, pp. 99-105, 2008.\u003c/li\u003e\n\u003cli\u003eJ. J. 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Cortese, \u0026quot;The neurobiology and genetics of Attention-Deficit/Hyperactivity Disorder (ADHD): what every clinician should know,\u0026quot; (in eng), \u003cem\u003eEuropean journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society, \u003c/em\u003evol. 16, no. 5, pp. 422-33, Sep 2012, doi: 10.1016/j.ejpn.2012.01.009.\u003c/li\u003e\n\u003cli\u003eA. A. Zevenbergen, \u0026amp; Whitehurst, G. J. , \u0026quot;Dialogic reading: A shared picture book reading intervention for preschoolers,\u0026quot; \u003cem\u003eOn reading books to children: Parents and teachers, \u003c/em\u003epp. 177-200, 2003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5003291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5003291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quality of parent-child interaction during shared reading may influence the activation and synchronization of reading-related brain networks. But could differences in brain activity while a child is listening to stories predict parent-child interaction level during reading? For this study, functional MRI including a stories listening task was performed with 22 4-year-old girls and behavioral measurement scores reflecting parent-child interaction as well as maternal depression levels affecting these interactions were collected using video observation data of a shared reading task of these children with their mothers. The study aim was to apply the fMRI stories-listening data to create a diffusion maps algorithm and then attempt to classify the level of parent-child interaction during a shared reading task outside of the scanner. The diffusion maps algorithm successfully clustered children in this manner, with higher parent-child engagement scores related to diffusion patterns in regions of the brain known to support reading. This study demonstrates that applying this diffusion maps algorithm to brain functional connectivity data can predict parent-child interaction during shared book reading. This algorithmic approach is a potential, novel, data-driven means to quantify parent-child interaction in different contexts (e.g., reading, play) and populations.\u003c/p\u003e","manuscriptTitle":"Functional connectivity of the sensory system and executive functions during a story listening task is related to parent-child interaction during joint reading: a functional MRI-diffusion map study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 08:47:13","doi":"10.21203/rs.3.rs-5003291/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-31T16:06:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-11T12:59:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-10T04:01:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-30T00:24:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13699267235437186925663994049240415570","date":"2024-09-18T07:19:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98367783839611951270173405927996479489","date":"2024-09-18T01:00:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66443257235733825747039568977567300437","date":"2024-09-16T12:46:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334663984203058859597398212912792489608","date":"2024-09-15T18:39:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-15T18:37:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-15T18:26:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-09T08:05:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2024-08-30T10:40:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"57a5b9af-cca6-4b26-9e6b-7009828065bb","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:04:10+00:00","versionOfRecord":{"articleIdentity":"rs-5003291","link":"https://doi.org/10.1007/s11682-025-01037-2","journal":{"identity":"brain-imaging-and-behavior","isVorOnly":false,"title":"Brain Imaging and Behavior"},"publishedOn":"2025-07-02 15:57:50","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2024-10-22 08:47:13","video":"","vorDoi":"10.1007/s11682-025-01037-2","vorDoiUrl":"https://doi.org/10.1007/s11682-025-01037-2","workflowStages":[]},"version":"v1","identity":"rs-5003291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5003291","identity":"rs-5003291","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}