Intrinsic insula network engagement underlying children's reading and arithmetic skills

Accepted Manuscript Intrinsic insula network engagement underlying children's reading and arithmetic skills Ting-Ting Chang, Pei-Hong Lee, Arron W.S. Metcalfe PII:

S1053-8119(17)30941-2

DOI:

10.1016/j.neuroimage.2017.11.027

Reference:

YNIMG 14474

To appear in:

NeuroImage

Received Date: 7 April 2017 Revised Date:

24 October 2017

Accepted Date: 15 November 2017

Please cite this article as: Chang, T.-T., Lee, P.-H., Metcalfe, A.W.S., Intrinsic insula network engagement underlying children's reading and arithmetic skills, NeuroImage (2017), doi: 10.1016/ j.neuroimage.2017.11.027. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Intrinsic insula network engagement underlying children’s reading and arithmetic skills

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Research Center for Mind, Brain & Learning

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Department of Psychology

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Ting-Ting Chang1,2*, Pei-Hong Lee2, Arron W.S. Metcalfe3,4

National Chengchi University

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Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto

Centre for Youth Bipolar Disorder, Sunnybrook Health Sciences Centre, Toronto, Canada

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Taipei, Taiwan

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Keywords: Insula, cognitive control, arithmetic, reading, resting state, connectivity, individual difference, salience network, central executive network

Running title: rAI network in children’s reading and arithmetic

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Address for Correspondence: T. –T. Chang, Ph.D.

Department of Psychology, National Chengchi University NO. 64, Sec. 2, ZhiNan Rd., Wenshan District, Taipei City 11605 Taiwan (R.O.C)

Email: [email protected]

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Abstract The neural substrates of children’s reading and arithmetic skills have long been of great interest to cognitive neuroscientists. However, most previous studies have focused on the contrast

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between these skills as specific domains. Here, we investigate the potentially shared processes across these domains by focusing on how the neural circuits associated with cognitive control influence reading and arithmetic proficiency in 8-to-10-year-old children. Using a task-free

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resting state approach, we correlated the intrinsic functional connectivity of the right anterior insula (rAI) network with performance on assessments of Chinese character recognition, reading

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comprehension, subtraction, and multiplication performance. A common rAI network strengthened for reading and arithmetic skill, including the right middle temporal gyri (MTG) and superior temporal gyri (STG) in the lateral temporal cortex, as well as the inferior frontal gyri (IFG). In addition, performance measures evidenced rAI network specializations. Single

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character recognition was uniquely associated with connectivity to the right superior parietal lobule (SPL). Reading comprehension only, rather than character recognition, was associated with connectivity to the right IFG, MTG and angular gyrus (AG). Furthermore, subtraction was

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associated with connectivity to premotor cortex whereas multiplication was associated with the supramarginal gyrus. Only reading comprehension and multiplication were associated with hyper

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connectivity within local rAI network. These results indicate that during a critical period for children’s acquisition of reading and arithmetic, these skills are supported by both intra-network synchronization and inter-network connectivity of rAI circuits. Domain-general intrinsic insular connectivity at rest contained also, functional components that segregated into different sets of skill-related networks. The embedded components of cognitive control may be essential to

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understanding the interplay of multiple functional circuits necessary to more fully characterize

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cognitive skill acquisition.

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1. Introduction Reading and arithmetic are considered as the most basic cognitive skills in formal education worldwide (Butterworth et al., 2011; Geary, 1994; OECD, 2010). The neural bases of how

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children learn to read and calculate have long been of great interest to cognitive neuroscientists, psychologists, and educators. Converging studies have focused on understanding the domain specificity of each of these cognitive capabilities (Ansari, 2008; Dehaene and Cohen, 2011;

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Dehaene et al., 2002; Dehaene et al., 2003; Kadosh et al., 2008). Less attention, however, has been paid to the shared cognitive processes and the associated neural mechanisms between

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reading and arithmetic, the most important of which may be cognitive control. Here we investigate how the putative cognitive control circuits support reading and arithmetic skills in 3rd and 4th graders, an important stage during which arithmetic and reading skills are beginning to

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master but have not yet fully matured (Chang et al., 2016; Yeatman et al., 2012).

During early stages of development, cognitive control plays a crucial role in building foundational cognitive skills (Geary, 2004; Shaywitz and Shaywitz, 2008; St Clair-Thompson

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and Gathercole, 2006). Cognitive control refers to the top-down mechanisms for voluntarily allocating mental resources based on internal goals and intentions (Buckner, 2004; Cai et al.,

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2015; Miller and Cohen, 2001). The experimental tasks used for assessing cognitive control usually require response inhibition, such as Stop-Signal, Go/NoGo, Flanker, as well as Stroop tasks (Cai et al., 2015; Levy and Wagner, 2011). Certain processes are necessary in helping children to establish building blocks before a basic skill is built into a long term knowledge representation, such as reading (Cantin et al., 2016; Houde et al., 2010; Locascio et al., 2010; Shaywitz and Shaywitz, 2008; St Clair-Thompson and Gathercole, 2006). Specifically, St Clair-

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Thompson & Gathercole found that the accuracy on both Stop Signal and Stroop tasks in 11year-old school age children was predictive of their school attainment in reading (St ClairThompson and Gathercole, 2006). Later on, Cantin and colleagues demonstrated that the

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correlation between Stroop task performance and reading comprehension can be found as early as 7 to 10 years of age (Cantin et al., 2016). Within this framework, children with reading difficulties have also been found to show deficits in tasks involving cognitive control (Cutting et

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al., 2009; Locascio et al., 2010). Locaiscio and colleagues found that 10-to-14-year-old dyslexic children, particularly those with reading comprehension deficits also showed comorbid deficits

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in executive function, including response inhibition, planning, and working memory (Locascio et al., 2010). The mechanism of cognitive control in reading is possibly related to young children with immature reading skills requiring extra mental resources to inhibit irrelevant information and enhance relevant concepts when extracting meaning from print in the early stage of reading

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development (Cantin et al., 2016).

Cognitive control has also been implicated in children’s arithmetic skills (Bull and Scerif, 2001;

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Cantin et al., 2016; Clark et al., 2010; Röthlisberger et al., 2013; St Clair-Thompson and Gathercole, 2006). In the study of St Clair-Thompson & Gathercole, 11-year-old school age

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children’s response inhibition performance was also predictive of their school achievement in mathematics (St Clair-Thompson and Gathercole, 2006). Bull and Scerif found that executive function, including Stroop and Wisconsin Card Sorting Task (WCST) performance explained high variance in mathematical ability, even after controlling for both reading and IQ scores (Bull and Scerif, 2001). Consistently, another two studies observed that inhibition, cognitive flexibility, as well as set shifting skills in preschool aged children can predict later mathematical

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achievement in elementary school (Clark et al., 2010; Röthlisberger et al., 2013). Collectively these studies establish that cognitive control facilitates arithmetic problem solving in children, perhaps by suppressing incorrect problem answers or unrelated distractors and strategies to

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identify correct answers (Bull and Scerif, 2001; Cantin et al., 2016). Although these previous efforts have intensely investigated the behavioral mechanisms of cognitive control in the acquisition of children’s reading and arithmetic skills, knowledge about the neural mechanisms

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and how the neural circuits of cognitive control facilitate acquisition of reading and arithmetic

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skills in children is still very limited.

Neuroimaging studies have provided abundant evidence that the neural bases of cognitive control predominantly involve several nodes in the fronto-cingular-parietal network, including anterior insula (AI), dorsal anterior cingulate cortex (dACC), dorsolateral prefrontal cortex

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(DLPFC), and posterior parietal cortex (PPC) (Cai et al., 2015; Levy and Wagner, 2011; Wager et al., 2005). The putative cognitive control brain network formed by this set of brain regions were most dominant in the right hemisphere (Cai et al., 2015; Sridharan et al., 2008; Uddin et al.,

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2011). These nodes can be isolated into two dissociable networks. The AI coupling with dACC form the major components of the salience network (SN) (Menon, 2015b; Seeley et al., 2007).

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This circuit has been implicated in subjective salience of external stimuli and in contributions to complex cognitive processes including central executive function as well as affective processes (Menon, 2015b; Menon and Uddin, 2010). The other circuit, central executive network (CEN), encompassing DLPFC as well as PPC (Menon, 2015a; Seeley et al., 2007). Unlike SN, the CEN and its nodes are engaged in information retention and manipulation during working memory, constructing problem solution, as well as goal-oriented decision making (Miller and Cohen,

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2001; Petrides, 2005; Rottschy et al., 2012). Converging studies investigating causal interactions between SN and CEN nodes during cognitive control tasks including Stop-Signal, Flanker task, inhibition and multi-digit calculation have revealed that brain signals are initiated from the

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anterior aspect of the insula toward other nodes within SN and CEN (Cai et al., 2017; Cai et al., 2015; Supekar and Menon, 2012). This data implies that the AI functions as the causal hub in the fronto-cingular-parietal cognitive control network and integrates information from other sub

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networks (Honey et al., 2007; Uddin et al., 2011). Although previous studies have demonstrated that the fronto-cingular-parietal circuits formed by SN and CEN are already firmly established

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in the early stage of the lifespan and continue to strengthen into adulthood (Gao et al., 2015a; Gao et al., 2015b; Supekar and Menon, 2012), it is not known how these networks facilitate children’s acquisition of domain specific cognitive skills. More specifically, it is not known how these networks are related to skill acquisition for reading and arithmetic cognition during 3rd and

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4th grades, the period crucial for development of reading and arithmetic proficiency (RosenbergLee et al., 2011a; Yeatman et al., 2012).

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The engagement of fronto-cingular-parietal network had been broadly associated with reading and arithmetic in children and adults (Arsalidou and Taylor, 2011; Houde et al., 2010; Menon et

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al., 2014; Supekar and Menon, 2012; Uddin et al., 2011). In one fMRI study, healthy adults showed greater bilateral but leftward AI activations when reading phrases compared to single words (Zaccarella and Friederici, 2015). In another adult study in which participants read consonant-vowel syllables generated with artificial grammar, the bilateral AI activated more when the rules of the syllable followed hierarchical structure rather than alternating sequence (Bahlmann et al., 2008). In a meta-analysis conducted on 26 fMRI studies involving distinct

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levels of linguistic tasks, the largest cluster was identified as in left insula (Ardila et al., 2014). Many other neuroimaging studies, including two meta-analyses, have consistently identified bilateral insula when adults and children performed arithmetic tasks (Arsalidou and Taylor, 2011;

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Houde et al., 2010; Menon et al., 2014; Supekar and Menon, 2012). In one developmental fMRI study, Supekar and Menon investigated the functional coactivation between all the prominent nodes of fronto-cingular-parietal network that showed right hemispheric dominance. They found

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that strong functional connectivity and causal interactions between the AI and dACC already had emerged during addition problem solving in children as young as 7-to-8 years of age (Supekar

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and Menon, 2012). Although these previous efforts have confirmed the engagement of frontoinsular-parietal circuits during cognitive skills and development, still no attempt has been made to examine how individual differences in reading and arithmetic skills are related to the intrinsic involvement of the fronto-cingular-parietal network during this critical phase of skill acquisition

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where cognitive control modulates performance prior to the establishment of long term knowledge representations (Bull and Scerif, 2001; Cantin et al., 2016; Clark et al., 2010; Röthlisberger et al., 2013; St Clair-Thompson and Gathercole, 2006). Further investigation of

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how this network supports reading and numerical problem solving is still needed.

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To address these issues, we collected fMRI data from 26 3rd and 4th graders. Previous studies have consistently demonstrated that the putative cognitive control network comprising SN and CEN is highly correspondent between nodes derived from rest and from cognitive control tasks (Cai et al., 2015; Crittenden et al., 2016; Smith et al., 2009; Supekar and Menon, 2012), suggesting that the functional architecture of these nodes are intrinsically configured as a coherent network. In order to tap into how this built-in organization associated with cognitive

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control influences children’s acquisition of reading and arithmetic skills, we investigate how intrinsic connectivity during a wakeful rest condition correlates with children’s reading and arithmetic skills. As previous endeavors have suggested that individual differences in brain

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activity provide a useful index for probing students’ school achievement (Price et al., 2013), here we use a similar approach by correlating reading and arithmetic performance with the resting state functional connectivity of the putative cognitive control network. We focused on the AI, the

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cortical hub of the fronto-cingular-parietal network and investigate how the AI circuits are predictive of cognitive skills. Our main goal was to identify the individual differences in

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arithmetic and reading skills that are similarly and distinctly associated with AI-connected nodes in 8-to-10-year-old children. Our investigation focused on three key aims. First, given that behavioral studies have found a fundamental role for cognitive control in children of this age (Cantin et al., 2016; Locascio et al., 2010), we hypothesize that both proficient reading and

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arithmetic performance would be associated with the strengthening of the AI circuits.

The second question we address is whether each of reading and arithmetic skills are associated

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with distinct functional AI circuits. For the reading assessments, we included single Chinese character recognition as well as context reading comprehension. Although both involve verbally

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retrieving phonology and semantic, the latter involve greater level of semantic integration from the context. We therefore hypothesize that individual differences of reading comprehension would be uniquely related to stronger coupling between AI network and brain regions that are functionally associated with semantic processing. We also investigate different components of arithmetic skills, including subtraction and multiplication, two operations that elicit largely distinct problem solving strategies. Multiplication relies to a greater extent on rote verbal fact

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retrieval, whereas subtraction involves procedural calculation and backup strategies (Campbell and Xue, 2001; Chochon et al., 1999). Thus we hypothesize that subtraction and multiplication would be uniquely associated with strengthing between AI and the strategic-specific brain

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networks.

Finally, we examine how the functional connectivity within the AI network would predict

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individual differences of reading and arithmetic skills. Behavioral studies have suggested that passage reading comprehension but not single word reading is associated with tasks involving

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cognitive response inhibition (Cantin et al., 2016; Sesma et al., 2009). Likewise, arithmetic involving procedural calculation but not fact retrieval was associated with inhibition and problem solving requiring higher-level executive functions (Bellon et al., 2016; Cantin et al., 2016; Gilmore et al., 2015; Sesma et al., 2009), we hypothesize that subtraction and reading

2. Methods

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2.1 Participants

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comprehension would exhibit stronger within AI network synchronization.

A total of 39 3rd and 4th graders were recruited from multiple school districts in Taipei City,

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Taiwan. All children were right handed with no reported history of psychiatric or neurological disorders and had normal or corrected-to-normal vision. Informed written consent was obtained from the legal guardian of each of the participating children. All of the study protocols were approved by the National Chengchi University Review Board. All participants were volunteers and were treated under the guidelines of the declaration of Helsinki. Among the participants, one child did not complete cognitive assessments. Twelve children had excessive head movements

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(the movement exclusive criteria see the section below). These exclusion criterion resulted in the final sample of 26 children (15 boys and 11 girls), with the age range from 8.3 to 10.7 (M = 9.5, SD = 0.6). All children included in the sample completed standardized cognitive ability

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assessments of nonverbal IQ, reading, and mathematical skills (see Table 1).

2.2 Standard assessments of cognitive abilities

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2.2.1 nonverbal IQ

To ensure the IQ of participants included in the study were within normal range, children’s

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nonverbal IQ was assessed using SPM-P (Standard Progressive Matrices – Parallel) (Raven et al., 2000), which can be administered to participants aged between 8 and 12. SPM-P consists of 60 figure patterns, each with one part being removed. Participants were instructed to choose the missing part from six to eight distractors. Each set involves different principles of matrix

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transformation, and within each set the items become more difficult. The number of correctly responded items was applied as nonverbal IQ measurement.

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2.2.2 Reading abilities

2.2.2.1 Chinese character recognition

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We assessed children’s reading abilities for single characters, as well as for context at the passage level. Chinese writing system is a morphosyllabic structure. The most basic unit of Chinese script is character rather than word. A character can be a word by itself, but can also be combined with other characters to form different words. Here we used the Chinese Character List (Hung et al., 2008) to examine children’s phonological and lexical knowledge of single Chinese characters. The Chinese Character Lists is a paper-and-pencil test commonly used to

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estimate how many characters each child knows. This test includes nationally standardized measures of academic reading skills for each grade in elementary school. The test was composed of characters selected from a pool of 5021 high frequency characters sorted as 17 levels based on

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their frequency. To ensure the difficulty of test items were evenly distributed across the character pool, a few characters were randomly selected from each level. During the assessments,

participants were first required to write down the pronunciation of each test item using Zhuyin, a

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phonetic-based alphabet system predominantly taught to children before they learn to read and write Chinese characters. Participants were also required to generate a multiple-character word

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using the test item. For example, for ‘天’ (pronounced as ‘tian’, means ‘sky’), participants may write down ‘天藍’ (pronounced as ‘tian-lan’, means ‘sky-blue’). The number of characters correctly responded for pronunciation and word generation were then used to generate a standardized estimated vocabulary score for the total pool of 5021 words (Hung et al., 2008);

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these estimates comprised the Character recognition score used in subsequent analyses.

2.2.2.2 Reading Comprehension

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We used the Reading Comprehension Screening Test (Ko, 1999) to measure participants

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reading skills of semantic integration and text inference at passage levels. This test also provides national norms for each grade of the academic year. During the first part of the assessment, participants read a sentence and were asked to replace certain characters or words with synonyms. In the second part, participants read a short passage and were asked to answer several questions based on the passage. The percentage of correctly responded items was used to estimate children’s reading comprehension ability.

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2.2.3 Subtraction and Multiplication tests To estimate children’s arithmetic skills, we created an arithmetic test similar to the French Kit (Ekstrom et al., 1976). We focused on subtraction and multiplication, because previous studies

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have suggested that these two operations are engaged in distinct strategies, as subtraction

requires more effortful calculation procedures whereas multiplication relies more on rote verbal fact retrieval (Chochon et al., 1999; Prado et al., 2011; Rosenberg-Lee et al., 2011b) even in

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young children (De Smedt et al., 2011; Prado et al., 2014). Our test battery consisted of 30

subtraction and 30 multiplication problems in separate sections. Each participant was given 2

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minutes per arithmetic operation section and instructed to solve the problems as quickly and accurately as possible. Number of correctly answered problems of each operation was computed as the index of children’s mathematical skill.

2.3.1 fMRI data acquisition

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2.3 Brain Imaging

In order to probe the intrinsic functional circuits, we collected fMRI imaging data using resting

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state fMRI. Participants were instructed to keep their eyes closed, lay still, stay awake, and let their mind wander without focusing on any specific thought in particular for the duration of the

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scan. This session lasted 6 minutes, resulting in a total of acquisition of 180 volumes for each participant.

Brain imaging data were acquired using a Siemens MAGNETOM Skyra 3T scanner at National Chengchi University in Taipei, Taiwan. During the scan, head movement was minimized using cushions placed around the participant’s head. We employed T2* weighted echo-planar

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sequences with the following parameters: TR = 2 s, TE = 35 ms, flip angle = 90°. The field of view was 256 x 256 mm, and the matrix size was 64 x 64, providing an in-plane spatial resolution of 4 mm. In the same scan session, high-resolution T1-weighted MRI sequences were

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acquired for each participant to aid localization of functional data. The following parameters were used: TI = 1100 ms; TR = 2530 ms; TE = 3.3 ms; flip angle = 7° ; 256 x 256 field of view;

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192 slices in axial plane; 256 x 256 matrix, yielding acquired resolution = 1 mm.

2.3.2 Movement

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We excluded children who had excessive head movement based on the following two criteria: (1) any of the x, y, and z directions beyond 5 mm; (2) 20% of mean frame-wise displacement (FD) greater than 0.5. The FD was calculated by summing the absolute values of the difference between two successive volumes of all the six motion parameters, as suggested by Power and

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colleagues (Power et al., 2012). The movement of the final participants range was on average of 0.32 (SD 0.20), 0.61 (SD 0.34), and 1.26 (SD 0.62) mm in the x, y, and z direction, with 0.39 (SD 0.18), 1.13 (SD 0.82), 0.31 (SD 0.22) degrees of roll, pitch, and yaw. None of the

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participants’ mean FD exceed 0.5, with the overall average of 0.18.

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2.3.3 fMRI data preprocessing

Functional MRI data were pre-processed using SPM8 (http://www.fil.ion.ucl.ac.uk/spm). Prior to statistical analysis, images were corrected for errors in slice-timing, realigned to the first image of each run to correct for head motion, coregistered to each of the individual participants’ structural scans, normalized to standard stereotaxic space (based on the Montreal Neurologic Institute coordinate system), and smoothed with a 6mm full-width half-maximum Gaussian

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kernel to decrease spatial noise. We conducted the “scrubbing procedure” proposed by Power and colleagues (Power et al., 2012) to correct for transient excesses in head movement. Deviant volumes were defined by FD value greater than 0.5 mm or derivative variance greater than 0.5%

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of global BOLD signal. These identified deviant volumes were then removed from further

2.3.4 fMRI Data analysis 2.3.4.1 Neurosynth region of interests (ROI)

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analysis.

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To identify ROIs associated with the putative cognitive control network, we first performed a Bayesian meta-analysis using the search term “cognitive control” on Neurosynth (Yarkoni et al., 2011). This analysis generated 428 studies. In order to obtain more stringent brain maps that are relevant to cognitive control tasks, we focused on reverse inference maps. The resulting brain

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map encompassed the conventional nodes of SN and CEN, including the right AI (rAI, volume size 1016 mm3), dACC (volume size 352 mm3), right DLPFC (rDLPFC, volume size 960 mm3), right VLPFC ( rVLPFC, volume size 296 mm3), and the right PPC (rPPC, volume size 736 mm3).

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As a dominant role of the rAI were extensively reported in previous studies and considered as the cortical hub for cognitive control (Cai et al., 2015; Sridharan et al., 2008; Supekar and Menon,

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2012; Uddin et al., 2011) and the rAI was the node with both the highest peak and greatest extent in the SN and CEN nodes identified from Neurosynth, here we focused on rAI as the seed ROI (Figure S1) in the following functional connectivity analysis.

2.3.4.2 Whole brain functional connectivity analysis

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Prior to the functional connectivity analysis, a 0.008 to 0.1 Hz bandpass filter was applied to the smoothed data to remove high frequency noise. We used a seed-based correlation approach that is similar to previously published studies (Jolles et al., 2016; Uddin et al., 2010) for the whole

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brain functional connectivity analysis. Time series were extracted by averaging across all voxels within the rAI seed. The resulting ROI time series was then used as a covariate of interest in an individual-level general linear model (GLM) to assess how strongly the time series fit the data at

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each voxel across the whole brain. Time series of white matter and cerebrospinal fluid signal along with 6 motion parameters, were used as additional nuisance covariates to remove

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confounding effects of physiological noise and participant movement. Group-level functional connectivity maps of rAI were then generated using one-sample t-tests of individual functional connectivity contrast images. In order to characterize the relationship between the individual differences of cognitive skills and the rAI circuits, group-level regression analysis was used to

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assess the relationship between each score of character recognition, reading comprehension, subtraction, and multiplication performance and the rAI functional connectivity maps. The significant clusters were defined using the voxel-wise height threshold of p < .01, and a spatial

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extent of p < .05, based on Monte Carlo simulations. For the purpose of visualizing the correlation, the Pearson’s correlation coefficients between behavioral measurement and the time

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series of the seed ROIs from each significant clusters were illustrated.

2.3.4.3 Spatial overlap between distinct cognitive measurements In order to examine rAI network that were similarly engaged by distinct cognitive measurements, we conducted the following analyses based on the within-network matching concept that were previously published (Gao et al., 2015a). Specifically, we first derived a binary mask using the

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rAI networks that were significantly associated with one of the to-be-compared cognitive measurement at the group level. Next, we examined the brain regions that were significantly correlated with the other cognitive measurement and rAI connectivity within this mask. The final

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significant regions were suggested as showing spatial overlap between distinct measurements and considered as similarly associated with the two cognitive measurements.

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Our analysis of spatial overlap between each cognitive measurements focused on two aspects. First, we examined the spatial overlap between reading and arithmetic skills. In this analysis we

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obtain a general index for each of the two cognitive skills by averaging the z-scores of the two subtests for both reading and arithmetic (Chang et al., 2015; Salthouse and Hedden, 2002). Using the above-mentioned method, the composite measurement of reading was used to generate a reading-related rAI connectivity map to examine arithmetic-related rAI connectivity within this

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mask. Using the same method, we then investigated the spatial overlap between the two reading subtests (character recognition and reading comprehension) as well as between the two

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arithmetic measures (subtraction and multiplication).

2.3.4.4 Spatial specificity of each cognitive measurement

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Next, to investigate the specificity of rAI circuit that was related with each of the measurements of reading and arithmetic skills, all the four measurements were entered as covariates into a group-level GLM analysis to assess rAI functional connectivity. The correlation between each measurement and rAI connectivity was then examined while all the other three measures were treated as covariates of no interest.

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2.3.4.5 Within-rAI network connectivity Finally, we tested the within-rAI network strengthening associated with each of the four cognitive measurement. In order to obtain a corresponding matured rAI network, we derive adult

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data from another ongoing project that were collected within the same scanner using the same acquisition parameters. We then conducted the above seed-based functional connectivity analysis and used the adult group-level significant rAI networks as reference to generate a binary mask.

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The within-rAI network connectivity of children was then defined as the mean functional

connectivity strength within the adult mask. Pearson’s correlation between the within-rAI

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network connectivity and each of the cognitive measurement was calculated, indicating the degree of the association between each cognitive measurement and the within-rAI network synchronization.

3.1 Cognitive assessments

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3. Results

Raw scores and percentiles of national norms (if provided) for nonverbal IQ, reading

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comprehension, character size estimation, as well as subtraction and multiplication test scores are presented in Table 1. Averages for all scores were in the normal range. As the sample spanned

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participants in both third and fourth grades, all subsequent analyses were performed using raw scores rather than percentiles. Zero-order correlations between each pair of the cognitive skill measures are presented in Table 2. Each of the measures were highly correlated with one another. All of the correlations remained significant after covarying out age and IQ score, except for the pair between subtraction and character recognition, indicating that behavioral

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performance was highly related between distinct measures of the same cognitive skills across

3.2 Brain Imaging 3.2.1 rAI network during resting state for children

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individuals.

In order to verify the intrinsic circuits of rAI, we first examined the functional connectivity of

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resting state rAI network in children using a seed-based correlation approach. Because rAI

circuits involve strong and widespread regions across the whole brain, group level functional

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connectivity maps of rAI were stringently thresholded using p < .05 family-wise error (FWE) correction at the whole-brain level, with cluster threshold of 30 voxels for illustration purposes. We found that rAI showed robust intrinsic functional connectivity with multiple regions, most predominantly in the major nodes of salience network including the right AI with adjoining

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VLPFC and ventral striatum, the left AI extending subcortically to ventral striatum, bilateral dACC, as well as anterior thalamus (Figure S2 and Table S1). These results confirmed that the seed identified from meta-analysis using the search term of cognitive control was a valid rAI

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seed and more critically, that this definition was pertinent in this sample of 8-to-10-year-old

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school-age children.

3.2.2 Relationship between rAI network and individual cognitive abilities 3.2.2.1 Character recognition Next, we examined how intrinsic rAI network in the resting state setting supported children’s cognitive skills by correlating the functional connectivity strength with different components of children’s behavioral measurements outside the scanner. We first entered the score of 3rd and 4th

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graders’ Character recognition as a covariate in the rAI functional connectivity analysis. Significant clusters that showed positive correlations between Character recognition and rAI intrinsic connectivity include the dACC (BA31; r = .727, p < .001), the left (r = .703, p < .001)

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and right STG with adjoining MTG (BA22; r = .653, p < .001), right postcentral gyrus ( BA3; r = .574, p = .002), right inferior frontal gyrus (IFG; BA44; r = .669, p < .001), and the left MTG (BA21; r = .559, p = .003; Figure 1 and Table 3). In order to validate the relation between

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Character recognition and rAI connectivity was not driven by IQ, age, or sex difference, we conducted additional regression analyses while covarying out the three variables on all of the

<= .036, Bonferroni corrected).

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significant clusters. Except for the left MTG, all the correlations remained significant (all ps

There were three clusters in the prefrontal cortex showing negative correlations between

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Character recognition and the rAI intrinsic connectivity: one in the right superior frontal gyrus (SFG; BA9; r = -.688, p < .001), another in the bilateral medial SFG (BA9; r = -.649, p < .001), and the other in the left MFG (BA8; r = -.591, p = 001). After regressing out IQ, age, and sex, all

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the clusters remained significant (all ps <= .015, Bonferroni corrected).

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3.2.2.2 Reading comprehension

We then entered the raw score of reading comprehension as a covariate in assessing rAI functional connectivity maps. As shown in Figure 2 and Table 4, we found strong positive correlation between reading comprehension and rAI connectivity in 3rd and 4th graders. Specifically, children with better reading comprehension showed higher rAI intrinsic connectivity with widespread bilateral brain regions, including dACC (BA24; r = .626, p = .001),

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a large cluster peaking in the right insula with the adjoining IFG and extending subcortically to thalamus all the way to the contralateral insula and right MTG as well as STG (BA13/41/43/45/21/22, r = .872, p < .001), the left MTG (BA21; r = .656, p < .001), right

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angular gyrus (AG; BA 40; r = .617, p < .001), as well as the right superior frontal gyrus (SFG; BA46; r = .572, p = .002). Likewise, we conducted regression analyses while covarying out IQ, age, and sex on all of the significant clusters. Except for the left MTG, right SFG, and dACC, all

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the correlation remained significant (all ps <= .049, Bonferroni corrected).

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Brain regions that showed negative correlation between reading comprehension and rAI connectivity included the left and the right medial superior frontal gyrus (BA8; r = -.667, p <.001), left superior parietal lobule (SPL) extending through precuneus to the right SPL (BA 7/5, r = -.762, p < .001), and the left MFG (BA6; r = -.547, p = .004). After regressing out IQ, age,

corrected).

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3.2.2.3 Subtraction

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and sex, only bilateral SPL and left MFG remained significant (all ps <= .039, Bonferroni

Next, we investigated the correlation between rAI connectivity with different components of

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arithmetic performance. We first included the raw score of correct subtraction task responses as a covariate in the whole brain rAI functional connectivity analysis. Brain regions that showed correlations between rAI connectivity and subtraction performance included the right insula extended posteriorly through frontal operculum to supramarginal gyrus (BA 13/40/41; r = .754, p < .001); right MTG (BA21; r = .573, p = .002), dACC (BA24; r = .648, p < .001), primary motor cortex (PMC; BA6; r = .645, p < .001), and left insula (BA13; r = .595, p = .001), see Figure 3

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and Table 5. We conducted regression analyses while covarying out IQ, age, and sex on all of the significant clusters. The correlation remained significant in all of the clusters (all ps <= .04,

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Bonferroni corrected).

Negative correlation between subtraction and rAI connectivity were observed only in cerebellum (r <= -.567, p <= .003). After regressing out IQ, age, and sex all cluster remained significant (all

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3.2.2.4 Multiplication

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ps <= .024, Bonferroni corrected).

We examined the correlation between correct multiplication performance and rAI connectivity. rAI connectivity positively correlated with multiplication in the left (r = .632, p = .001) and right MTG (BA22; r = .629, p = .001), the left (r = .668, p < .001) and right IFG (BA44; r = .738, p

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< .001), the left supramarginal gyrus (BA 40; r = .575, p = .002), and the right insula (BA13; r = .660, p < .001) , see Figure 4, Table 6. We conducted regression analyses while covarying out IQ, age, and sex on all of the significant clusters. The correlation remained significant in all of

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the clusters (all ps <= .04, Bonferroni corrected).

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Negative correlation between multiplication and rAI connectivity were observed in the left cerebellum (r = -.567, p = .003) and the medial SFG (BA10; r = -.619, p = .001). After regressing out age, IQ, and sex, the left cerebellum was marginal (p = .054, Bonferroni corrected) and the medial SFG remained significant (p = .004, Bonferroni corrected).

3.2.3 rAI circuits that are similarly engaged in distinct cognitive measurement

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3.2.3.1 Reading and arithmetic Next, we investigate shared rAI connectivity between distinct cognitive measurements. We first examine the regions that are similar between reading and arithmetic skills. As shown in Figure

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5A and Table 7, brain regions that are both positively associated with reading and arithmetic include the right STG with adjoining MTG extending all the way to SMG, and the right IFG with

not show any overlap.

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3.2.3.2 Character recognition and reading comprehension

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adjoining insula. Negative correlation with reading and arithmetic-related rAI connectivity did

Next, we investigated rAI connectivity that are positively associated with both the reading measurements. As shown in Figure 5B and Table 7, only one cluster remained significant: the right inferior frontal gyrus. Negative correlation of character recognition and reading

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comprehension associated rAI connectivity map did not display any overlap.

3.2.3.3 Subtraction and multiplication

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Positive association of rAI connectivity with both the arithmetic test was also assessed. As shown in Figure 5C and Table 7, the brain regions associated with both subtraction and

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multiplication include the right IFG, right STG with adjoining SMG and the right STG with adjoining MTG. No regions survived when assessing overlap between negative correlations.

3.2.4 Specificity of rAI circuits associated with reading and arithmetic skills We also investigated the specificity of rAI circuit associated with each cognitive skill by examining correlation between each measurement and rAI connectivity while regressing out all

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the other three measures. In this analysis, we found that Character recognition uniquely correlated with rAI connectivity to the right superior parietal lobule (SPL). Reading comprehension, in contrast, correlated with widespread regions including the right STG

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extending posteriorly to AG, IFG, anterior temporal cortex, and the bilateral cerebellum

extending anteriorly to thalamus. For the arithmetic tasks, subtraction uniquely correlated with rAI connectivity to the PMC in the right hemisphere. Multiplication, in contrast, showed unique

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correlation with rAI connectivity to the left supramarginal gyrus (Figure 6, Table 8).

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3.2.5 Within-rAI network strengthening of each cognitive measurement

Next, we tested the how each cognitive measurement was associated with local rAI network connectivity. Only reading comprehension (r = 0.523, p < 0.05, Bonferroni corrected) and multiplication (r = 0.543, p < 0.05, Bonferroni corrected) were associated with strengthening of

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within-rAI network connectivity. In contrast, subtraction (r = 0.415, p > 0.05, Bonferroni corrected) and character recognition (r = 0.335, p > 0.05, Bonferroni corrected) did not reach significance. Pair-wise comparisons of correlation coefficients revealed no significant

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differences between all the four cognitive skills (p >= .107).

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3.2.6 Reading- and arithmetic-related connectivity with other nodes of SN and CEN We assessed whether reading- and arithmetic-related functional circuits were specific to the rAI seed by conducting parallel analyses using other prefrontal seeds within SN and CEN, including dACC, rDLPFC, and rVLPFC. The results showed that only the pattern of arithmetic- and reading- related dACC connectivity were similar with rAI connectivity. More specifically, character recognition was associated with the hyper connectivity between dACC and MTG, STG,

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IFG, and SPL; reading comprehension was associated with higher connectivity between dACC and MTG, IFG, and dACC; subtraction was associated with the bilateral PMC and dACC; multiplication was associated with IFG, MFG, as well as SMG (Table S2, Figure S3). Results of

behavioral measurements (Table S2, Figure S4, S5).

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other seeds were either not significant or showing minimal correlation with all the four

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3.2.7 Relationship between lAI network and individual cognitive abilities

Despite that the putative cognitive control network has been extensively demonstrated as

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showing a right hemispheric dominance in the literature, the left insula is indeed a critical node within the frontal-cingulate parietal network. It is not known whether the association between the left AI (lAI) and the cognitive measurements are weaker than or as robust as the right hemisphere. To address this question, we conducted parallel analyses by first flipping the rAI

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seed to the left and used the flipped ROI as the seed region. The results are similar to but weaker than the rAI seed. Specifically, character recognition is associated with lAI connectivity with dACC, bilateral STG, and right IFG. Reading comprehension was associated with lAI and

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bilateral MTG, IFG and insula. Subtraction was associated with the bilateral MTG, STG, dACC, as well as right PMC. Multiplication was associated with the left MTG, right IFG, and SMG

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(Figure S6, Table S3).

4. Discussion

The current study investigated the rAI circuit organization across individuals and how it associated with single character reading, passage reading comprehension, subtraction and multiplication arithmetic during a critical period for acquisition of these skills in 8-to-10-year-old

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3rd and 4th graders. Although behavioral assessments revealed high similarity between these measurements, we found individual differences in reading and arithmetic skills are characterized by both shared and specific rAI functional connectivity with a right hemispheric dominance.

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Both reading and arithmetic performance showed strong associations with the rAI connectivity to the right MTG, STG, as well as IFG. Importantly, reading and arithmetic skills also

demonstrated specificity in rAI functional connectivity. Although both the reading subtests were

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associated with the right IFG, character recognition was uniquely recruited an right SPL

association whereas reading comprehension assessing contextual interpretation of whole

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passages was uniquely correlated with rAI connectivity to largely spread regions, including the right AG, IFG, and MTG. For arithmetic problems, although both the operations were associated with the right IFG as well as the STG, further differentiation was observed with subtraction showing unique connectivity to the PMC, whereas multiplication was associated with unique

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connectivity to the left SMG. These results suggest both a common and specialized profile of rAI circuits in children learning arithmetic and reading skills. Collectively the current findings are the first attempt using fMRI resting state to demonstrate the functional role of putative cognitive

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control circuits in supporting children’s construction of building basic cognitive abilities. Below

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we discuss the implications of our findings.

4.1 Better reading and arithmetic skills are associated with strengthened rAI connectivity The central finding of this study is that the correlation between rAI circuits and cognitive skills vary across individuals during the developmental acquisition periods. Positive correlation indicates cognitive measurement is associated with strengthened rAI connectivity, whereas negative correlation reflects inefficient rAI connectivity. We found the trend that all of the four

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cognitive measurements are positively associated with widespread rAI connectivity, indicating the facilitating effects of the local rAI strengthening on cognitive reading and arithmetic

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development during the early stage.

The commonalities of the rAI network profile of association with these cognitive skills mainly included the right MTG and STG in the lateral temporal cortex as well as IFG in the PFC. The

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bilateral IFG along the sylvian fissure with MTG and AG formed the canonical language

network (Shirer et al., 2012). Neuroimaging studies assessing task-based brain responses have

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associated the left IFG with effortful articulatory programming of phonological coding during reading (Fiez and Petersen, 1998; Pugh et al., 2000, 2001). This area was found to show greater engagement during reading of pseudowords and real words with irregular spellings (e.g., mint) relative to highly familiar real words (Fiez and Petersen, 1998). The bilateral MTG and STG

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within the lateral temporal cortex, in contrast, was linked to semantic processing during reading context (Binder et al., 2011; Humphries et al., 2007; Kocagoncu et al., 2017). The MTG has been considered as the anchor of long-term storage of semantic memory and plays a critical role in

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integrating semantic information with task performance (Binder and Desai, 2011; Blumenfeld et al., 2006; Fiebach et al., 2002; Rossell et al., 2003). This region is also recruited when perceiving

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semantically congruent sentences compared to sentences generated by random word sequences (Humphries, Binder, Medler, & Liebenthal, 2006) as well as perceiving familiar real words compared to pronounceable pseudowords without actual semantic information (Fiebach et al., 2002). Consistently, transcranial magnetic stimulation administered to bilateral lateral temporal cortex has also been found to impair semantic decision, such as in a synonym judgment task (Lambon Ralph et al., 2009). In line with the task studies, intensified intrinsic connectivity

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between IFG and MTG has been reported as associated with better reading competence in both children and adults (Koyama et al., 2011). Our results further linked the strengthening of rAI and reading-related circuits with reading skills, suggesting the necessity of the top-down control of

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phonology decoding and semantic integration during children’s extraction of meaning from print, both at the word and passage level. This network recruitment of rAI and language circuits may

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be an underappreciated component of brain networks in the reading literature.

Bilateral MTG connectivity with the rAI was also associated with both arithmetic measures.

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Previous task-based imaging and lesion studies have demonstrated the crucial engagement of medial and lateral aspects of temporal regions during numerical problem solving (Baldo and Dronkers, 2007; Cho et al., 2011; Julien et al., 2008). In one study assessing arithmetic processing of 2nd and 3rd graders who used different strategies to solve simple addition problems,

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children that primarily used retrieval strategies were found to show significantly different multivariate activity patterns in right medial temporal lobe when compared to those who primarily used counting strategies (Cho et al., 2011). Consistently, in another study examining

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semantic dementia patients with either left and right temporal lobe atrophy, Julien and colleagues observed that these patients showed deficits in arithmetic operations requiring semantic fact

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retrieval such as multiplication (Julien et al., 2008). Beyond the regional localization level, Cho and colleagues found significant functional connectivity between ventrolateral prefrontal cortex (VLPFC) and right medial temporal lobe during children’s addition problem solving (Cho et al., 2012). Together these results have supported the close link between semantic memory and arithmetic knowledge, anchored in the fronto-temporal circuits. Brain regions along the circuits later upsurge synchronization (Chang et al., 2016) and develop convergence across distinct

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arithmetic problems (Chang et al., 2015) as children achieve proficiency of arithmetic skills. Collectively these findings further suggest that not only the regional localization of the frontal and temporal regions but also the entire circuits are crucial in the development and the

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maturation of children’s arithmetic skills.

Common circuits across reading and arithmetic in the temporal lobe collectively, has been

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reported in one previous study assessing reading and arithmetic performance of patients with left hemisphere stroke (Baldo and Dronkers, 2007). In that study, the authors found that the extent of

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the patients’ left posterior temporal cortex atrophy were highly correlated with their performance of arithmetic and reading comprehension. These results have provided structural evidence for common neural substrates of reading and arithmetic performance localized in the MTG. Our findings further provide evidence that the associated network is potentially bilateral. The neural

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processing mechanism does not only show structural basis, but that the level of coactivation with the rAI network seed at rest shows that these inherent connections emerge early and are putatively integral to level of performance on fundamental skills. Further studies are needed to

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capture the growth pattern of the cognitive-related structural integrity within the circuits.

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The cognitive control studies had been extensively localized in the right hemisphere (Cai et al., 2015; Levy and Wagner, 2011; Sridharan et al., 2008; Supekar and Menon, 2012; Uddin et al., 2011). The language- and reading-related task engagement of the insula had been implicated in the bilateral and leftward (Ardila et al., 2014; Bahlmann et al., 2008; Zaccarella and Friederici, 2015). The arithmetic-related task engagement of the insula was, in contrast, localized in the bilateral and rightward insula (Arsalidou and Taylor, 2011; Houde et al., 2010; Supekar and

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Menon, 2012). Our results provide evidence that a common substrates shared by reading- and arithmetic-related network are centralized in the rAI, suggesting the domain-general cognitive control network is essential in facilitating both the cognitive skills during this critical period of

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learning epoch. Importantly, consistent with the literature, although the lAI produce very similar profile, the results of the lAI were weaker than the right hemisphere (Cai et al., 2015; Uddin et al., 2011). We therefore argue that the relation between lAI and each of the cognitive

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measurement is a byproduct of the rAI through the internal connection that helps to establish the

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proficiency in building the foundational cognitive development.

4.2 Specific association between reading performance and rAI connectivity Our results also highlight network specialization emerging in the development of skill acquisition. The first being a distinct profile of rAI connectivity globally related to the reading

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assessments. Both passage reading comprehension and single character recognition were associated with the right IFG. Importantly, single character recognition was uniquely associated with connectivity to the right SPL, the region involved with spatial attention engagement

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(Dehaene et al., 2003). These results are possibly due to the high demands of visual feature discrimination and spatial processing when reading logographic stimuli. In contrast, passage

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comprehension was associated with increased extent of rAI connectivity to large-scale languagerelated regions, including IFG, AG, and MTG. The hyper rAI connectivity links with passage reading comprehension is consistent with the interpretation that more demanding and effortful processing was needed when reading passages with contextual information in contrast to single character recognition (Sesma et al., 2009).

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Although the language processing has been conventionally focused on the left hemisphere, still extensive studies have reported activations of the right language network, e.g. IFG, MTG, and AG in distinct level of linguistic tasks (Binder et al., 1997; Chai et al., 2016; Fedorenko et al.,

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2010; Wehbe et al., 2014). Consistently, brain atrophy in the right hemisphere can cause severe linguistic problem such as understanding or make inference of story (Beeman and Chiarello, 1998). One imaging study had explicitly modeled brain responses in the right IFG, MTG and AG

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during comprehension task. The results suggested that the right hemisphere is indeed coactivated with the left but with higher level of signal flexibility (Chai et al., 2016). Our results therefore

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supported that reading skill is greater supported by the within-hemisphere strengthening of rAI network. How far the lateralization persists on the developmental progression still needs further investigation.

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Interestingly, only character recognition and reading comprehension were negatively associated with rAI connectivity to frontoparietal circuits. A developmental progression of shifting from frontal to posterior dorsal and ventral visual streams across reading development had been on

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record in the literature (Morken et al., 2017; Shaywitz et al., 2007; Yeatman et al., 2012), suggesting that young children rely more on PFC whereas adults depend on dorsal and ventral

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streams during reading. Our results have further supported that reading skills of young children is characterized by disengagement of the frontoparietal circuits.

4.3 Operation-specific association between arithmetic performances and rAI connectivity Analysis of rAI arithmetic circuits revealed further evidence that functional generality and specialization for subtraction and multiplication, two operations involving distinct problem

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solving strategies (Campbell and Xue, 2001; Chochon et al., 1999; De Smedt et al., 2011; Prado et al., 2011; Rosenberg-Lee et al., 2011b), had emerged with children learning. Subtraction problems are generally solved by using calculation procedures as well as manipulating abstract

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quantity (Campbell and Xue, 2001; Chochon et al., 1999; De Smedt et al., 2011; Prado et al., 2011; Rosenberg-Lee et al., 2011b). The out-scanner performance of this operation correlated with connectivity between rAI and PMC, the region responsible for motor response. This region

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has also been consistently implicated in arithmetic problem solving (Menon et al., 2014). One likely explanation is that a commonly used strategy for arithmetic problem solving is finger

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counting. Interrupting hand movement was found to disrupt arithmetic problem solving, even for adults (Crollen and Noel, 2015; Imbo et al., 2011). During the initial stage of learning arithmetic problems, finger counting and other backup strategies were even more extensively used by children, especially when solving subtraction problems (Barrouillet et al., 2008; Siegler, 1987;

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Siegler and Shrager, 1984). We therefore suspect that children rely on the strong coupling between rAI and its strategy-specific associated function to achieve up-regulated subtraction

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performance.

Multiplication, in contrast, correlated with the connectivity of the left SMG. The answers of

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multiplication problems are mostly retrieved by rote verbal fact retrieval as the operation was predominantly learned by memorizing table facts. In line with these previous conclusions drawn from behavioral literature, our results demonstrate that multiplication was correlated with rAI connectivity to the SMG. The SMG in the PPC coupling with PFC are typically engaged by cognitive tasks that require manipulation of information in phonological working memory (Kwon et al., 2002). Our findings therefore provide novel evidence to suggest that the SMG

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together with PFC form a memory-related functional circuit to facilitate the verbally mediated retrieval of multiplication facts from memory in the development of arithmetic knowledge for children. Collectively, the current study suggests that distinct components of arithmetic problems

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engage heterogeneous components of the cognitive control network, and hence provides insights into utilization of domain general mental resources for arithmetic learning.

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4.4 Reading comprehension and multiplication are associated with within-AI network strengthening

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Another interesting finding is that only reading comprehension and multiplication involving higher level of local rAI network connectivity. The insula-connected circuits are formed by several nodes, with the functional signal initiated from rAI toward dACC, DLPFC, as well as VLPFC. The dACC together with insula constitute major nodes of the SN responsible for

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detecting stimulus saliency in the outside world and organize into top-down input for the frontalcingular system (Cai et al., 2015; Menon, 2015b; Supekar and Menon, 2012; Uddin et al., 2011). This network has been mainly associated with response inhibition (Cai et al., 2015; Xue et al.,

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2008; Zhong et al., 2014) and involve in a wide variety of cognitive processes, such as language (Bahlmann et al., 2008; Kreisler et al., 2000; Saygin et al., 2004), attention (Trautwein et al.,

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2016), and arithmetic (Supekar and Menon, 2012). Animal studies have identified that the structure of the insula has extensive connections with the entire brain, including premotor cortex, somatosensory and inferior parietal lobe in the parietal cortex, superior temporal sulcus in the temporal cortex, as well as subcortical regions including amygdala, perirhinal cortex, thalamus, and cingulate cortex (Augustine, 1996; Preuss and Goldman-Rakic, 1989). DLPFC makes up the major component of the CEN (Menon, 2015a; Seeley et al., 2007), serves in information

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retention and manipulation during working memory, constructing problem solution, as well as goal-oriented decision making (Miller and Cohen, 2001; Petrides, 2005; Rottschy et al., 2012). Given the characteristics of the functional variety and the structural connections, the entire set of

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SN and CEN formed the putative cognitive control circuits and had been linked with higher-level cognitive control and in contributions to complex cognitive processes including central executive function as well as mental task efforts such that more difficult tasks would elicit greater

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engagement of insula (Dosenbach et al., 2006; Menon, 2015b; Menon and Uddin, 2010).

Consistently, more error-prone and effortful task types are associated with greater extent of

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insula activation (Bahlmann et al., 2008; Supekar and Menon, 2012). Our results demonstrated that strong functional connectivity within this network was associated only with passage reading comprehension but not character recognition, consistent with previous behavioral literature whereby passage reading comprehension rely on high level of cognitive control (Cutting et al.,

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2009; St Clair-Thompson and Gathercole, 2006). Surprisingly, within-cognitive control network was also found in multiplication but not subtraction. We suspect that it is because subtraction is taught prior to multiplication and likely develops proficiency early. Solving multiplication

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problems is hence possibly still effortful for 3rd and 4th graders and engage great extent of rAI

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within-network connectivity.

Notably, our results are specific to rAI and dACC seeds, but not other seeds within the prefrontal cortex, such as VLPFC and DLPFC. These results further support that the cognitive controlrelated prefrontal circuits can be dissociated into distinct intrinsic networks (Seeley et al., 2007). Together these findings suggest that during this critical period for skill acquisition, skill level acquisition can be described across domains as the strengthening of rAI-specific control hub

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connections throughout the internal whole-brain network. Further studies are necessary to more precisely characterize how the functional architecture of cognitive control circuits for each

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individual assists children’s learning.

4.4 Conclusion

For the past several years, neuroimaging studies have switched from focusing on localization of

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brain responses associated with specific cognitive functions to investigations of neural circuits spanning multiple distributed brain regions. The approach of using intrinsic connectivity to

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investigate large scale brain networks has also become increasingly promising. This work has resulted in advances in uncovering the biological underpinnings of children’s learning and skill acquisition. Toward understanding the ubiquitous skills of reading and arithmetic, however, the vast majority of efforts have focused on domain specificity and relevant brain regions. Here, we

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have provided an alternative framework, dissecting common and distinct network contributions and highlight the importance of the large-scale brain circuits affording cognitive control to characterize the patterns of neural coupling that lead to children’s acquisition of these

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foundational cognitive skills. Moreover, using a task-free resting state approach, the current study supported that the brain bases of skill acquisition are intrinsically wired in the neural

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substrates. Critically, we found evidence for distinct connections with rAI circuits underlying children’s learning of reading and arithmetic. Our findings support that reading and arithmetic skills are afforded by distinct rAI connections that provide network explanations for relatively well-known behavioral phenomena. Such brain processes may provide basic building blocks for the maturation of cognitive skill acquisition. Our approach further provides novel evidence of concurrent domain general and task-specific systems involved in cognitive skills development

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with the rAI as a central hub in both those domain general and domain specific functions. Future studies tracking longitudinal trajectories are needed to capture the growth patterns of each

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cognitive capability for each individual.

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Acknowledgements We thank Yutin Lin for her assistance in data acquisition and analysis and four anonymous

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reviewers for their valuable feedback. This research was supported by grants from the Ministry of Science and Technology (grant numbers MOST 103-2511-S-004-004, 104-2511-S-004 -004,

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105-2511-S-004 -001 -MY3).

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Figure Captions Figure 1. Brain regions that showed correlation between rAI connectivity and character recognition. Character recognition was correlated with increased intrinsic rAI connectivity with

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the bilateral superior temporal gyri (STG) and the dorsolateral anterior cingulate cortex (dACC) as well as decreased connectivity with superior frontal gyrus (SFG). Scatter plots are based on functional clusters identified using whole-brain regression analysis, and are provided for the

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purpose of visualization. RSFC, resting state functional connectivity. *** p < .001.

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Figure 2. Brain regions that showed correlation between rAI connectivity and reading comprehension. Reading comprehension was correlated with increased intrinsic rAI connectivity with distributed brain areas, including the dorsolateral anterior cingulate cortex (dACC), bilateral inferior frontal gyrus (IFG), bilateral middle temporal gyrus (MTG), and right

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angular gyrus (AG) and decreased connectivity in middle frontal gyrus (MFG) and superior parietal lobule (SPL). The scatter plots are based on functional clusters identified using wholebrain regression analysis, and are provided for the purpose of visualization. RSFC, resting state

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functional connectivity. ** p < .01. *** p < .001.

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Figure 3. Brain regions that showed correlations between rAI connectivity and subtraction. Subtraction performance was correlated with increased intrinsic rAI connectivity with the dorsolateral anterior cingulate cortex (dACC), the right insula, primary motor cortex (PMC) and the\middle temporal gyrus (MTG) and decreased connectivity with cerebellum. Scatter plots are based on functional clusters identified using whole-brain regression analysis, and are provided

50

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for the purpose of visualization. RSFC, resting state functional connectivity. ** p < .01. *** p < .001.

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Figure 4. Brain regions that showed correlations between rAI connectivity and

multiplication. Multiplication performance was correlated with increased intrinsic rAI

connectivity with the bilateral inferior frontal gyrus (IFG) and the middle temporal gyri (MTG).

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Scatter plots are based on functional clusters identified using whole-brain regression analysis, and are provided for the purpose of visualization. RSFC, resting state functional connectivity. **

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p < .01. *** p < .001.

Figure 5. Spatial overlap between brain regions that showed association with rAI connectivity and each cognitive measurements. (A) Reading and arithmetic performances

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were associated with rAI connectivity to the right MTG and IFG. (B) Both character recognition and reading comprehension were associated rAI connectivity with the right IFG. (C) Both subtraction and multiplication were associated with the rAI connectivity to the right MTG as

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well as the right IFG.

Figure 6. Brain regions that showed specificity of correlation between rAI connectivity and each cognitive skill. (A) Character recognition uniquely associated with right superior parietal lobule (SPL), whereas (B) reading comprehension uniquely associated with angular gyrus (AG), middle temporal gyrus (MTG), as well as (IFG). (C) Subtraction exclusively correlated with the

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right premotor cortex (PMC), whereas (D) multiplication exclusively associated with

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supramarginal gyrus (SMG).

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ACCEPTED MANUSCRIPT Table 1. Raw and percentile rank based on national norms of IQ, reading, and arithmetic performance scores for children. Raw

Character recognition Reading comprehension Arithmetic Subtraction Multiplication

SD

M

SD

26(15) 9.5

0.6

-

-

41.0

8.1

49.2

29.6

2490.5 69.7

975.3 17.6

66.0 67.3

29.2 25.1

20.6 21.4

6.1 5.3

-

-

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Measure IQ Reading

M

SC

N(Males) Age

Percentile rank

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Variable

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Note: Only IQ and reading measurements have national norms provided.

1

ACCEPTED MANUSCRIPT Table 2. Pearson’s correlation between character recognition, reading comprehension, subtraction and multiplication. Reading Character recognition

Reading Character recognition Reading comprehension

-

Reading comprehension

Subtraction

Multiplication

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Variable

Arithmetic

0.51**

0.43*

0.57**

-

0.58**

0.59**

-

0.67*** -

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Arithmetic skill Subtraction Multiplication

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out age and IQ. *p < .05; ** p < .01; *** p < .001.

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Note: Underlined numbers refer to correlations no longer significant after regressing

2

ACCEPTED MANUSCRIPT Table 3. Brain regions that showed correlation between rAI connectivity and character recognition. Region

BA

Positive correlation L dorsal Anterior Cingulate Cortex

# of

peak

voxels T-score

MNI coordinate x

y

z

362

4.76

-12

-14

48

L Superior/Middle Temporal Gyrus 22 R Superior Temporal Gyrus / 41/40 Supramarginal Gyrus R Postcentral Gyrus / Precentral Gyrus 3/4

298

4.53

-50

-18

-10

272

4.48

56

-22

10

3.91

26

-34

62

R Inferior Frontal Gyrus L Middle Temporal Gyrus

44 21

226 106

3.77 3.72

30 -64

8 -44

22 -8

Negative correlation R Superior Frontal Gyrus

8/9

4.65

14

42

36

4.08 4.06

-4 -20

58 34

18 42

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116

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31/32

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312

L/R Medial Superior Frontal Gyrus L Superior / Middle Frontal Gyrus

10/9 8

369 109

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Note: BA, Brodmann area. L, left. R, right.

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ACCEPTED MANUSCRIPT Table 4. Brain regions that showed correlation between rAI connectivity and reading comprehension. BA

Positive correlation L/R Insula / Putamen / Thalamus /

# of

peak

voxels T-score

7238

-

364

L Middle Temporal Gyrus R Anterior Temporal Cortex R Angular Gyrus R Superior Frontal Gyrus

21 21 40 46

213 157 201 117

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L/R Superior Parietal Lobule / Precuneus L Superior / Middle Frontal Gyrus

y

z

6.95

28

18

4

5.15

-18

-48

-28

4.53 3.96 3.73 3.55

-46 48 62 52

-40 18 -56 36

2 -24 32 14

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3/45/21 /22

Negative correlation L/R Medial Superior Frontal Gyrus

x

13/41/4

Pallidum / Inferior Frontal Gyrus / Frontoparietal operculum / R Middle/Superior Temporal Gyrus Cerebellum

L/R dorsal anterior cingulate cortex

MNI coordinate

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Region

24

113

3.29

4

-16

46

8

147

5.85

0

40

52

7/5

1559

4.99

-20

-56

62

6

122

3.44

-26

0

64

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EP

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Note: BA, Brodmann area. L, left. R, right.

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ACCEPTED MANUSCRIPT Table 5. Brain regions that showed correlation between rAI connectivity and subtraction. BA

Positive correlation R Frontoparietal Operculum/

40/41/

# of

voxels T-score

6 22/21

396 215

L/R dorsal Anterior Cingulate Cortex L Insula

24 13

179 163

Negative correlation R Cerebellum

231

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-

1348

L Cerebellum L/R Cerebellum

-

419 114

MNI coordinate x

y

z

4.61

42

4

12

4.58 4.24

40 44

-18 -30

42 -2

4.24 3.62

-2 -38

-12 -2

44 8

5.41

28

-46

-50

4.28 4.00

-46 -6

-60 -46

-50 -44

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Supramarginal Gyrus / Insula / Putamen /Superior Temporal Gyrus R Primary Motor Cortex R Middle/Superior Temporal Gyrus

13

peak

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Region

AC C

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Note: BA, Brodmann area. L, left. R, right.

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ACCEPTED MANUSCRIPT Table 6. Brain regions that showed correlation between rAI connectivity and multiplication. Region

BA

# of

peak

voxels T-score

MNI coordinate x

y

z

5.26

62

-28

16

5.24 4.30

42 -44

10 36

16 10

3.99

-48

2

12

Parietal Lobule R Insula R Middle Temporal Gyrus

289 159

44

275

22/21 40

161

3.81

-54

-42

6

119

3.69

-64

-32

44

13 21

112 127

3.68 3.51

42 52

2 -46

-8 -8

10

222 106

3.56 4.66

-40 2

-72 64

-32 14

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L Frontoparietal Operculum / Inferior Frontal Gyrus L Middle/Superior Temporal Gyrus L Supramarginal Gyrus / Inferior

949

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Supramargnal Gyrus / Frontoparietal 40/41 Operculum R Inferior Frontal Gyrus 44 L Inferior Frontal Gyrus 46

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Positive correlation R Superior/Middle Temporal Gyrus /

Negative correlation L Cerebellum L/R Medial Superior Frontal Gyrus

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Note: BA, Brodmann area. L, left. R, right.

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ACCEPTED MANUSCRIPT Table 7. Brain regions that were overlapped between distinct cognitive measurements. Brain Regions

BA

# of

peak

MNI coordinate

voxels T-score

x

y

z

5.1

34

10

22

4.6

48

-28

8

A) Reading & Arithmetic R Inferior Frontal Gyrus / Insula R Superior/Middle Temporal Gyrus / Frontoparietal Operculum / Supramarginal Gyrus

13/44

268

40/41/ 668 21

SC

Negative correlation

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Positive correlation

N. S. Positive correlation R Inferior Frontal Gyrus Negative correlation N. S. C) Subtraction & Multiplication Positive correlation

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B) Reading comprehension & Character recognition

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R Frontoparietal Operculum / Inferior Frontal Gyrus

44/45

138

3.8

30

8

22

44

137

4.6

42

4

12

42/40

328

4.4

44

-22

18

22

169

4.2

44

-30

-2

R Superior Temporal Gyrus /

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Frontoparietal Operculum / Supramarginal Gyrus

R Superior/Middle Temporal Gyrus Negative correlation

AC C

N. S.

Note: BA, Brodmann area. L, left. R, right. N. S.: not significant.

7

ACCEPTED MANUSCRIPT Table 8. Brain regions that showed correlation between rAI connectivity and each cognitive skill while covarying out other measures.

A) Character recognition R Superior/Inferior Parietal Lobule / Angular Gyrus B) Reading comprehension L/R Cerebellum / Thalamus / Pallidum / Putamen / Insula

R Anterior Temporal Cortex R Medial Superior Frontal Gyrus / Superior Frontal Gyrus C) Subtraction R Primary Motor Cortex

TE D

D) Multiplication L Supramargnal Gyrus

peak

voxels T-score

7/40

293

-/13

4862

3.63

MNI coordinate x

y

z

34

-62

58

9.47

0

-54

-32

5.88

28

18

4

5.63 4.43

58 -26

-40 28

-2 -4

112

3.94

52

12

-34

199

3.84

8

42

38

6

351

4.70

38

-12

38

7/40

804

4.86

-32

-46

42

13/44/ 41 22/21 13

509 1127 110

M AN U

R Insula / Putamen / Inferior Frontal Gyrus / Frontoparietal operculum/ R Middle/Superior Temporal Gyrus L Insula / Putamen

# of

RI PT

BA

SC

Region

21 9/10/3 2

AC C

EP

Note: BA, Brodmann area. L, left. R, right.

8

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT