From Schools to Scans: A Neuroeducational Approach to Comorbid Math and Reading Disabilities.

We bridge two analogous concepts of comorbidity, dyslexia-dyscalculia and reading-mathematical disabilities, in neuroscience and education, respectively. We assessed the cognitive profiles of 360 individuals (mean age 25.79 ± 13.65) with disability in reading alone (RD group), mathematics alone (MD group) and both (comorbidity: MDRD group), with tests widely used in both psychoeducational and neuropsychological batteries. As expected, the MDRD group exhibited reading deficits like those shown by the RD group. The former group also exhibited deficits in quantitative reasoning like those shown by the MD group. However, other deficits related to verbal working memory and semantic memory were exclusive to the MDRD group. These findings were independent of gender, age, or socioeconomic and demographic factors. Through a systematic exhaustive review of clinical neuroimaging literature, we mapped the resulting cognitive profiles to correspondingly plausible neuroanatomical substrates of dyslexia and dyscalculia. In our resulting "probing" model, the complex set of domain-specific and domain-general impairments shown in the comorbidity of reading and mathematical disabilities are hypothesized as being related to atypical development of the left angular gyrus. The present neuroeducational approach bridges a long-standing transdisciplinary divide and contributes a step further toward improved early prediction, teaching and interventions for children and adults with combined reading and math disabilities.

Introduction

The classification, diagnosis, and treatment of learning disabilities are important topics of research in both psychoeducational and neuroscience literature. Researchers in these two fields often measure similar constructs but use differing approaches to work with individuals with learning disabilities. Consequently, each field has produced different concepts and theories over time, leading to a sort of disconnect between the identification of learning disabilities in educational settings and the identification of learning disabilities based on neuroscientific evidence, respectively. Furthermore, the identification and development of comorbid learning disabilities, while a prevalent topic in psychoeducational literature, remains relatively understudied in neuroscience; a testable model of the neuroanatomical substrates of comorbidity is greatly needed. We developed a novel neuroeducational approach to bridge the corresponding concepts on learning disabilities in the two disciplinary fields.

Learning Disabilities

Learning disabilities are a type of neurodevelopmental disorders that impede the acquisition, retention, or application of verbal or non-verbal information, affecting a person's ability to use specific cognitive skills (1, 2). The most prevalent learning disability is reading disability, a specific difficulty in learning to read, interpret, and manipulate written words, also known as dyslexia. The second most prevalent is mathematical disability, a specific difficulty in learning arithmetic and performing mental calculations, also known as dyscalculia (3, 4).

Current research on comorbid math and reading disabilities and their developmental origins is far from exhaustive. As recently as 2007, a systematic review of the U.S. Department of Education's Educational Research Information Center (ERIC) database revealed that the number of published studies on reading disability outnumbered the number of studies on math disability by a ratio of 14 to 1 (5). This disparity in knowledge translates into disproportionate diagnoses and asymmetric interventions for individuals with comorbid math and reading disabilities. Therefore, defining a robust neuroeducational model of dyslexia-dyscalculia comorbidity is a priority for the early identification and treatment of learning disabilities (6–8).

The Psychoeducational Approach to Identifying Learning Disabilities

The psychoeducational evaluation is the traditional method of classifying and identifying learning disabilities. The goal of the evaluation is to examine the student's performance on standardized tests of general academic achievement (9), and will determine if the student qualifies for special education or remedial training (10). A psychoeducational assessment typically consists in obtaining an IQ score and selected domain-specific standardized tests—psychometric measures that directly assess abilities in reading, writing, or arithmetic (8, 11).

In this approach, the IQ-achievement discrepancy criterion provides the framework for identifying an unexpected difficulty with learning. To be classified as having a learning disability, the discrepancy model requires that there is a significant discrepancy (usually 1.5 standard deviations) between the person's academic ability or potential (defined by the IQ score) and academic achievement (as defined by their scores on a general reading or math test). This model rests on the questionable assumption that intelligence tests are not confounded by more basic processes for which domain-specific psychoeducational tests provide independent measures (12), and regrettably exclude the possibility of identifying learning disabilities in people with intellectual disabilities (13).

Notably, a study by Tanaka et al. (14) reported evidence based on brain activity demonstrating the diagnostic inappropriateness of the IQ discrepancy criterion. Replicating previous findings based on psychoeducational tests (15) they showed that brain activity and structures associated with reading difficulties in individuals with intact general intellectual ability and in individuals with lower intellectual ability show very similar profiles. The latter supports the recent removal of the IQ discrepancy from the definition of specific learning difficulties in the DSM-V.

The Neuropsychological Approach to Identifying Learning Disabilities

Compared to psychoeducational evaluations, neuropsychological assessments are greater in the depth of their assessment. They are more fine-tuned to examine specific cognitive deficits (such a phonological processing deficits) that underlie learning disabilities. A neuropsychological assessment is performed by licensed clinical neuropsychologists who combine elements of brain anatomy, cognitive neuroscience, and neurodevelopment to infer the neurological correlates of differences in specific cognitive abilities (7).

Secondly, neuropsychological assessments are greater in the breadth of the assessment. In contrast to a psychoeducational evaluation (which typically consists of an IQ score and a few standardized tests), a full neuropsychological assessment includes a structured clinical interview with the client (and interviews with the client's family and/or significant others, if possible), a review of the client's relevant medical records, and the administration of tests that measure domain-general functions such as selective attention, sensory perception, fine motor skills, visuospatial reasoning, and working memory (7). All the available information will be used to make a specific diagnosis of the client's learning disability, instead of relying on psychometric measures alone.

Third, the interpretation of test scores from a neuropsychological assessment is guided by different principles than in a psychoeducational evaluation. Instead of applying the IQ-discrepancy model as in the psychoeducational approach, clinical neuropsychologists define the severity of a learning disability by introducing a cut-off threshold on the tail end of a distribution of academic achievement (9). While cut-off points are useful for providing a post-assessment diagnosis, given the limits of current causal models, they do not precisely reflect the neurobiological basis of a learning disability (13, 16); they rather emphasize normativity and address pragmatic issues related to early intervention.

The identification of math and reading disabilities is a predominant topic in neuroeducation—an emerging field at the intersection of neuropsychology, neuroscience, and psychoeducational research (17–19). The goals of neuroeducation are to develop curricula and teaching methods that are based on a scientific understanding of neural mechanisms of learning (20, 21). In line with this paradigm, the central theme of the present paper is linking in the most direct way possible corresponding constructs in education and neuroscience. The aim is to improve prediction of the biological and psychological factors that yield poor academic outcomes seen in individuals with learning disabilities. By examining the cognitive profiles of individuals with dyslexia and dyscalculia and mapping the observed deficits to their neuroanatomical correlates from existing research, the neuroeducational model proposed in the current research provides educators and neuroscience researchers with a working framework for designing effective teaching and interventions specific to individuals with comorbid learning disabilities.

The Neuropsychology of Dyslexia

Approximately 10% of North American children experience developmental dyslexia (22, 23), a disorder characterized by difficulties with reading fluency that are not better explained by visual or cognitive impairments, psychosocial challenges, or poor language instruction. Observable symptoms include inaccurate or effortful reading, poor spelling ability, and the avoidance of leisure or work-related activities that involve reading (22, 24).

The Cognitive Profile of Reading Disability and Dyslexia

There are several overlapping theories about dysfunctional cognitive processes that impair reading fluency in developmental dyslexia (25), however three theories have garnered widespread support in current research literature: the phonological deficit theory, double-deficit theory and the visual deficit theory.

The phonological deficit theory is the most widely-promoted and well-established theory in dyslexia research (26, 27). This theory proposes that the core impairment in dyslexia is a deficit in phonological processing—a pervasive difficulty with forming associations between phoneme combinations and the correct corresponding sounds, known as grapheme–phoneme correspondence (28, 29). Deficits in phonological processing can be identified early in development (30, 31) and persist into adulthood (32, 33). Dyslexic children exhibit marked difficulties in manipulating pseudowords (non-sensical words made up of valid phonemes in a particular language), and display poor reading fluency when asked to read written words, but not when the words are read to them by another individual (6, 23). In addition, specific training to improve phonological processing leads to significant improvements in reading ability (34, 35).

The double-deficit theory of dyslexia builds on the notions presented in the phonological deficit theory. In addition to impaired phonological processing, this theory suggests dyslexia is characterized by a deficit in rapid automatized naming (RAN). RAN is the measure of how quickly an individual can recognize and name aloud a series of familiar objects, pictures, colors, or symbols, or letters (36). While recent studies have suggested that poor RAN performance may reflect impaired functional connectivity between brain structures that control visual processing and speech, poor RAN performance in sight-word reading can indicate phonological deficits in individuals with dyslexia and are more likely to underlie the difficulties in recognizing words (37, 38).

The visual deficit theory states that reading disabilities arise due to atypical development of the visual system, whereby there is disruption in the processing of visual information from letters and words in written text. Some neuropsychological studies have shown that individuals with reading disabilities exhibit impaired temporal processing, atypical eye movement regulation, and more frequent visual scanning errors in comparison to normal readers (39). While below-average performance on visual attention tasks in preschool has been shown to predict reading disability (40), it is unclear whether a visual system deficit is a root cause or a result of long-term reading disabilities (41), and seems to contradict recent findings of heightened visuospatial reasoning in dyslexic adults (42–44).

The Neuroanatomical Correlates of Dyslexia

Converging evidence from functional neuroimaging studies has pinpointed three neuroanatomical regions in the left hemisphere which primarily facilitate the multimodal processing of written words: the left inferior frontal gyrus, the fusiform gyrus, and temporoparietal parietal junction (displayed in Figures 1A,B). The inferior frontal gyrus contains Broca's area, a region that is well-known in neuropsychological literature for its mediating role in speech production, but less recognized for its role in processing phoneme sequences and phonological segmentation (46–48). The fusiform gyrus (also known as the occipitotemporal gyrus) contains the Visual Word Form area, which enables humans to distinguish between the symbols that form letters and numbers, and symbols that are otherwise arbitrary shapes (49, 50). The temporoparietal junction (a group of structures including the angular gyrus, supramarginal gyrus, and the superior temporal gyrus) facilitates semantic processing and is also involved in the analysis of phoneme sequences (16, 51, 52).

Figure 1

(A,B) Three neuroanatomical structures of the left hemisphere language processing network. (A) The left inferior frontal gyrus and the left angular gyrus are highlighted in this lateral view of the cerebral cortex. (B) Sagittal view of the cerebral cortex shows the left fusiform gyrus. (C) Lateral view of the left intraparietal sulcus. While activation of several brain regions correlates with various aspects of mathematical cognition, intraparietal sulcus (IPS) is the primary activation site during tasks that test numerical magnitude processing (see text). Brain diagrams were adapted from (45). Anatomy of the Human Body. Retrieved from https://www.bartleby.com/107/189.html.

Dyslexia is associated with atypical development of the left hemisphere language network. Compared to age-matched controls, individuals with dyslexia show atypical physiological activity and white-matter connectivity in several frontal, parietal, and temporal structures in their dominant hemisphere (23, 26, 53). Studies that used functional neuroimaging to examine the neural correlates of phonological decoding consistently found that individuals with dyslexia typically exhibit lower cerebral blood oxygenation at the posterior regions of their language network—usually the left fusiform gyrus and the structures of the left temporoparietal junction (6, 16). Meta-analyses of neuroimaging studies that compared functional brain abnormalities between individuals with dyslexia have identified a variety of other brain regions that exhibit atypical activity during reading tasks. A meta-analysis of 28 studies identified hypoactivation of the left inferior frontal gyrus, left fusiform gyrus, left temporoparietal cortex, left occipitotemporal cortex, left precuneus, left frontal operculum, left precentral gyrus, and right superior temporal gyrus, as well as hyperactivation in the left anterior insula (54). Two other meta-analyses identified atypical hypoactivity in bilateral superior temporal gyri, left middle and left inferior temporal gyri, left precuneus, left thalamus, right postcentral gyrus, and the right fusiform gyrus during reading tasks (55, 56).

The Neuropsychology of Dyscalculia

Developmental dyscalculia is characterized by difficulties in processing numerical information and performing basic mathematical operations, impeding the acquisition of age-appropriate mathematical skills (24, 57). It is estimated that dyscalculia affects 3–6% of the world population (58, 59), but dyscalculia is considerably unrepresented in research literature on learning disabilities [5, (57)]. Anywhere from 17 to 66% of individuals with dyscalculia also fit the diagnostic criteria for dyslexia (60, 61). Indeed, students with a math disability are just over two times more likely to also have a reading disability than those without a math disability (62).

The Cognitive Profile of Mathematical Disability and Dyscalculia

Dyscalculia is characterized by impaired non-symbolic and symbolic numerical processing, the ability to quickly estimate and manipulate numerical magnitudes and quickly perform mental operations without writing out procedures (63) or relying on verbally-based strategies such as counting (64, 65). The most popular view of mathematical cognition, and consequently of math disabilities [i.e., Triple Code Model; (66, 67)], entail that all development of symbolic number skills derive (through alternative format re-coding), and are ultimately grounded on the innate endowed ability of “number sense.” Accordingly, humans would form mental representations of numerical quantities using a mental number line, an imaginary line of numbers ordered in an ascending series. Thus, an individual would estimate the place any number or quantity on the number line and perform operations using their approximation of the number—a cognitive function known as numerical magnitude processing or the approximate number system (ANS). The acuity of a person's ANS is often measured using numerical magnitude comparison tasks with non-symbolic quantities (e.g., a group of dots) as opposed to symbolic Arabic digits (e.g., the number 9). In a non-symbolic numerical comparison task, the individual is asked to approximate the correct place for non-symbolic quantity (without counting each item one-by-one) on a visually presented number line. A greater degree of error in ANS has been identified as the core deficit underlying developmental dyscalculia (68, 69). In comparison to typically-developing controls, children with dyscalculia demonstrate lower accuracy in approximating the number of non-symbolic items in a group, and lower accuracy in determining which group of items is greater in magnitude (70, 71). ANS is assumed to rely heavily on spatial representations of numbers (72, 73); individuals with dyscalculia often perform poorly on neuropsychological tests of visuospatial ability (74).

However, a survey of the spectrum of quantity cognition for animals and humans (75) shows that number sense can directly account only for a fraction of the acquired skills, mainly involving approximate small (subitizing for numerosities 1–4) and large quantity assessment and comparison. Oral and written language account for most of the learning spectrum [see Figure 2 in (75)]. On this background, the “primacy” of the number sense has been most recently challenged, since the bulk of multidisciplinary existing evidence demonstrates that the alleged mapping between number sense and symbolic, more complex notions are not as direct as postulated (for example in the most influential Triple Code Model). A review of current neuroscience and behavioral evidence (76) suggests that several alternative possible and plausible routes of normal and atypical non-symbolic to symbolic correlations could occur which provide a better empirical account of math achievement than direct effects of number sense.

One perspective alternative to ANS contends that numerical ability is grounded on representing, understanding, and manipulating symbol-symbol associations (SSA). That is, small numerical symbols are initially mapped on a precise representation (e.g., the subitizing range) which, supported by increasing counting and linguistic competency, eventually leads to an independent and exact symbolic system based on order relations between symbols (77). Most magnitude estimation and comparison effects found in studies confirming ANS can be equally explained and modeled in terms of the SSA, and there is also sufficient evidence of distinct brain mechanisms associated to symbolic and non-symbolic numerical processing (78). Further, recent meta-analyses show that symbolic numerical processing tasks are a strong predictor of arithmetic and have consistently been found to be deficient in dyscalculia [see, for instance (79)]. Critically, representatives of this alternative view of numerical cognition differentiate between a non-symbolic deficit and an access deficit in dyscalculia, which reflects intact ANS, but deficient access to number semantics from numerical symbols (65).

At the same time, a wealth of evidence in research and practice shows that during formal schooling children with dyscalculia experience learning challenges in symbolic and linguistic-based quantitative reasoning related to academic mathematics such as arithmetic, not only numerical skills. These difficulties include learning and remembering exact number words and concepts, and applying skills in: addition/subtraction, multiplication and fraction strategies, commutation and percentages, using place value, as well as geometry, time, measurement, and word problems (80). Without discounting the important role that basic numerical processing might play, the scope of the present work is more narrowly focused on the latter higher-level symbolic and linguistically-based quantitative abilities as they are more directly linked with achievement in educational settings (64).

The Neuroanatomical Correlates of Dyscalculia

The current literature of brain imaging studies reveals that the brain recruits a wide variety of interconnected regions during mathematical tasks, including prefrontal, posterior parietal, occipito-temporal, and hippocampal areas (81). Neuroimaging studies on individuals with dyscalculia have identified two parietal regions associated with the manipulation of numerical quantities: the bilateral intraparietal sulci and the left angular gyrus. Multiple functional neuroimaging studies have shown that the right and left intraparietal sulci (shown in Figure 1C) become activated during calculation tasks that involve numerical magnitude processing (69, 82, 83). In contrast, the angular gyrus becomes activated during the retrieval of arithmetic facts from long-term memory, such as when finding the solutions to simple multiplications (84, 85).

While the neuroanatomical evidence of atypical brain function in developmental dyscalculia is not quite exhaustive, several functional neuroimaging studies have reported atypical activation patterns at the intraparietal sulci. Compared to age-matched controls, children with dyscalculia exhibit reduced activation at the right intraparietal sulcus when performing non-symbolic numerical comparison tasks [for example (86)]. In addition, applying TMS to the right intraparietal sulcus can severely impede performance on numerical magnitude tasks, artificially producing deficits that are equivalent to those observed in adults with dyscalculia [for example (87, 88)].

An Overview of Cognitive Profiles of Comorbid Math and Reading Disabilities

A body of work has focused on the cognitive profiles of individuals with comorbid math and reading disabilities, establishing a design (here dubbed as the “four-groups design”) which has become pivotal in this research area. Specifically, Willburger et al. (89) examined cognitive performance in children with reading disability alone (RD), mathematical disability alone (MD), or with comorbidity of both disabilities (MDRD) as compared to typically developing and/or achieving children (TD and/or TA). MDRD children exhibited additive deficits in rapid automatized naming; this suggested that the deficits associated with comorbidity are additive and not qualitatively different from the deficits in the single disabilities. Later, this team (90) examined how domain-specific processes (e.g., symbolic and non-symbolic numerical processing, phonological processing) and domain-general processes (e.g., working memory, computations) contribute to comorbidity. MDRD children exhibited domain-specific deficits in phonological processing and numerical magnitude processing, performing at the same level as individuals with RD or MD. Unexpectedly, both the latter groups demonstrated better short-term working memory than the MDRD and the TD group, hinting that some domain-general processes may contribute to comorbidity.

Furthermore, Moll et al. (91) showed how three domain-general processes—namely processing speed, temporal processing, and verbal memory—can correlate differentially with reading and mathematical performance and are also associated with inattentive behavior. Both RD and MD children exhibited deficits in verbal memory. However, after controlling for parent-reported difficulties with inattention, deficits in verbal processing became associated with reading ability alone, whereas slowed temporal processing and visuospatial memory deficits were associated with mathematical ability alone. The authors concluded that deficits in processing speed, temporal processing, and verbal memory reflect variations in subclinical attention difficulties, and that reading and mathematical disabilities may thus be the outcome of multiple impaired cognitive systems rather than individual domain-specific processes. Relatedly, Wilson et al. (74) demonstrated that MDRD adults exhibited additive deficits in rapid naming and working memory, equivalent to the sum of the deficits exhibited by adults with the single disabilities. These authors concluded that additive domain-general deficits were likely correlates (not the underlying cause or the eventual consequence) of comorbidity.

More recent studies have shown that some processes traditionally considered as domain-specific may play an important role in comorbidity. Slot et al. (92) showed that children's rapid automatized naming and phonological awareness were associated with reading performance, whereas number sense and visuospatial working memory were associated with mathematical performance. However, phonological awareness was also predictive of mathematical performance, suggesting that a shared deficit in phonological processing may underlie both RD and MD. Similarly, Raddatz et al. (93) found that MD children showed deficits in various non-verbal and verbal tasks related to number processing, whereas RD children showed deficits in verbal tasks.

In contrast to this literature, neuroscience studies have rarely adopted the four-groups design. Improving on this limitation, the following study was designed to start filling some gaps in clarifying the nature of domain-specific and domain-general deficits of reading-math comorbidity with reference to the currently known neural underpinnings of dyslexia and dyscalculia.

The Present Study: Research Questions, Design and Hypotheses

The primary objective of this study was to outline a neuroeducational model of dyslexia-dyscalculia comorbidity—a framework for understanding the psychoeducational and neuropsychological characteristics of individuals with comorbid math and reading disabilities that can be tested for validity in future research. The neuroeducational model proposed here is [as defined by (94)] a preliminary theory or set of hypotheses to synthesize current knowledge and then guide and refine evidence-based practice in education, public health and the allied fields. It should not by any means be interpreted as proof of established knowledge or theory. We fully expect this “probing” model to be tested and re-tested and in this process modified, refined, or even falsified based on future research. This model was established in three phases. In the first phase—using a psychoeducational approach—we examined the performance of individuals with math and reading disabilities on a series of psychoeducational tests and drew conclusions about the specific cognitive deficits they exhibited. In the second phase—using a neuropsychological approach—we performed a systematic review of existing clinical studies on the neuroanatomical correlates of dyslexia and dyscalculia; then, we identified the involvement of key neuroanatomical structures displaying abnormal function. In the third phase, we mapped the deficits as measured by psychoeducational tests to their most plausible neuroanatomical correlates obtained in the systematic review, creating a neuroeducational model of comorbidity that unites the broad psychoeducational definitions of math and reading disabilities with neuropsychological evidence of the biological characteristics of dyslexia and dyscalculia. This series of operations allowed us to build correspondence between the diagnostic tools used to identify learning disabilities in psychoeducational context and the neurodevelopmental theories of dyslexia and dyscalculia in the neuropsychological literature.

To determine the cognitive profile of comorbid dyslexia-dyscalculia, performances were measured from a sample population with math disability, reading disability, and dual math, and reading disability via a comprehensive battery of psychoeducational tests. The statistical analyses of their psychoeducational outcomes were used to (i) determine if there were any measurable cognitive deficits that were unique (in nature or in magnitude) to the participants with comorbid reading-math comorbidity; (ii) determine if the cognitive deficits were domain-specific (within the realm of reading or numerical cognition) or domain-general (working memory and/or executive functions outside the realm of reading or numerical cognition) in nature; (iii) determine the nature of the relationship between math and reading deficits in the comorbid group. Two sets of hypotheses were assessed:

First set of hypotheses: It was hypothesized that the deficits in the comorbid participants would either be additive (where the approximate sum of the deficits in the reading-disabled participants and the math-disabled participants is measured), synergistic (an over-additivity caused by an interaction between math and reading deficits), or antagonistic (an under-additivity caused by an interaction between math and reading deficits). The nature of the relationship between math and reading disabilities was determined using the same 2 × 2 factorial design previously used in the four-groups design literature (74, 90). A significant interaction between the math disability and reading disability indicates a synergistic over-additivity or an antagonistic under-additivity in the mean scores of a particular test, and lack thereof indicates an additive effect.

Second set of hypotheses: it was hypothesized that the comorbid participants in this study would exhibit: (1) impaired reading fluency and phonological processing equivalent to those shown by individuals with reading disability alone, (2) impaired quantitative reasoning skills equivalent to those shown by participants with mathematical disability alone, and (3) deficits in working memory equivalent to those shown by individuals with reading disability alone. Consequently, it was also hypothesized that, consistent with the proposed neuroeducational approach, it should be possible to derive a mapping of correspondence between the pattern found in the psychoeducational findings and known neuroanatomical correlates in the clinical neuroimaging literature, which can be empirically tested with further neuroimaging studies.

Materials and Methods

Psychoeducational Tests

Tests of Achievement for Identifying Math and Reading Disabilities

Wide Range Achievement Test 3rd edition (WRAT3) arithmetic subscale

The WRAT3 Arithmetic subscale is a test of written arithmetic problems, which included number addition, subtraction, multiplication, and problems involving fractions and decimals.

Wide Range Achievement Test 3rd edition (WRAT3) reading subscale

The Reading subscale is a single word reading test, where participants were asked to read aloud a series of increasingly difficult words.

Testing Phonological Processing

Rosner Auditory Analysis Test

The Rosner Auditory Analysis test is the first of two Phoneme Deletion tasks included in this study. Participants were instructed to repeat a list of 40 common English words. Next, the test administrator asked the participant to repeat each word without pronouncing a specific phoneme, thereby “deleting” the first, last or embedded phoneme from the word and pronouncing the word fragment(s) that remained.

Pseudowords Phoneme Deletion task

In this second Phoneme Deletion task, participants were instructed to listen to 30 pseudowords and then repeat it by “deleting” a specific phoneme.

Woodcock Reading Mastery Tests-Revised (WRMT-R) Word Attack subtest

The Word Attack subtest examines a participant's phoneme-grapheme awareness without relying on a verbal demonstration by the test administrator (95). Participants were instructed to read a list of 45 pseudowords. The level of difficulty gradually increased throughout the test; the number of syllables in each pseudoword increased intermittently from 1 syllable to 4 or 5 syllables by the end of the list.

Testing Quantitative Reasoning

KeyMath revised, interpreting data subtest

Participants completed the Interpreting Data subtest of the revised KeyMath Assessment (KeyMath-R) (96). Participants were tasked with solving a written mathematical problem (i.e., “Kareem can read sixty pages in two and one–half hours. How many pages can he read in 1 hour?”).

Testing Intellectual Functioning

All participants, aged 17 and over, completed three subtests of the Wechsler Adult Intelligence Scale (WAIS-R) (97). Participants aged 6 to 16 completed three analogous subtests from the Wechsler Intelligence Scale for Children (WISC-III) (98).

WAIS-R/WISC-III vocabulary subtest

The Vocabulary subtest measures a person's semantic memory retrieval. Participants performing the WAIS-R were asked to orally define a series of 30 vocabulary words, gradually increasing in difficulty. Participants performing the WISC-III were asked to name pictures representing each word.

WAIS-R/WISC-III Block Design subtest

In the Block Design subtest, participants were asked to re-create a model or a picture of a design using up to nine red and white blocks within a time limit. This test was included as a measure of visuospatial reasoning.

WAIS-R/WISC-III Digit Span subtest

The Digit Span subtest examines verbal working memory. Participants were presented orally with a series of single-digit numbers. In the first half of the trials, they were required to orally repeat the presented numbers in the same order they heard (forward digit span); in the second half of the trials, they were to repeat the presented numbers in the reverse order (backward digit span).

Procedure

Participants were tested individually for a 3 h session (including two 10 min breaks). Each testing session began with the administration of the Vocabulary, Block Design, and Digit Span subtests from the WAIS-R or the WISC for participants aged 6–16. Successively, after the first break, each participant completed the Reading, Spelling, and Arithmetic subscales of the WRAT3. After the second break, each participant completed a series of psychoeducational tests. All participants completed four tests of phonological processing: The Rosner Auditory Analysis task, the Pseudowords Phoneme Deletion task, followed by the Word Attack and Word Identification subtests of the Woodcock Reading Mastery Tests (WRMT-R), the latter being excluded from this analysis. Lastly, participants completed the KeyMath Interpreting Data subtest.

Sampling

The participants in this study were selected from a database resulted from a 10 year prospective cohort research study at the University of British Columbia (UBC). A total of 585 participants ranging from 7 to 77 years of age were recruited from around the Greater Vancouver Area as well as the graduate and undergraduate student population at UBC. They were recruited through a publicly advertised free comprehensive psychoeducational assessment offered as compensation for their participation and in exchange for use and publication of the resulting anonymous group data and findings. This study was approved by the UBC institutional research ethics boards in accordance with the 1964 Declaration of Helsinki ethical standards and in strict adherence of the Tri-Council Policy Statement (https://ethics.gc.ca/eng/policy-politique_tcps2-eptc2_initiatives.html). Participants or their parents/guardians (for children < 12 years of age), signed a consent form; parental/guardian's consent was conditional on children's active assent. Participants or parents/guardians completed a brief questionnaire on demographic and socioeconomic information about themselves or their family.

The testing format varied over the decade of data collection, and over 50 different types of test scores were entered into database. The initial database was reduced so as to only include the participants who completed specific psychometric tests in the same specific format and which therefore provided information about the cognitive profiles of individuals with dyslexia and dyscalculia permitting to test the objectives of this study.

Inclusion criteria for the present study entailed: (1) completion of The WRAT3 Arithmetic subscale and WRAT3 Reading subscale, which served as the main diagnostic indicators; (2) completion of domain-specific tests that examine phonological processing (the reading domain) or quantitative reasoning (the mathematical domain), and domain-general tests that examine executive functions (such as working memory and spatial reasoning). In the mathematical domain, only one test was selected: the Interpreting Data subtest of the revised KeyMath Assessment; (3) completion of three neuropsychological tasks that test domain-general cognitive functions were selected: The Vocabulary, Block Design, and Digit Span subtests of the WAIS-R (for participants ages 17 and older) or the WISC-III (participants ages 16 and younger); and finally (4) Estimated IQ scores [calculated using the sum of the WAIS-R Vocabulary subtest and the WAIS-R Block Design subtest, as in (12)] had to be > 70, which we adopted as the clinical threshold for low IQ (99).

The final analysis included data from 360 participants. The average age of the participants (on the day of testing) in each group are presented in Table 1. A one-way ANOVA followed by Tukey post-hoc multiple comparisons revealed that the average age of the RD group was significantly lower than the average age of the TA group (p = 0.008), the MD group (p = 0.017) and the MDRD group (p = 0.007). A two-way MANCOVA was conducted to examine the effect of age on the scores from all seven psychoeducational tests, with math disability and reading disability as the independent variables and age as a covariate. Using the Bonferroni procedure to correct for multiple ANOVAs (with a significant threshold of p < 0.008), there were no significant interactions between age and math disability, nor between age and reading disability, on the mean scores for any of the psychoeducational tests (Wilks' Lambda = 0.012). Furthermore, a three-way MANOVA was conducted with math disability, reading disability, and age category as independent variables. The participants were divided into two age categories: below age 16 and above age 16. MD × Age Category interaction was not significant (Wilks' Lambda = 0.780) nor was the RD x Age Category interaction Wilks' Lambda = 0.349). As a result, the low average age of the dyslexic did not appear to have a significant effect on the psychoeducational test results as whole.

Table 1

Characteristics of the four groups.

	TA	MD	RD	MDRD	All groups
N	158	69	46	87	360
Mean age (years)	26.47 (14.66)	26.80 (12.69)	19.22 (10.96)	27.22 (12.99)	25.79 (13.65)
n Female	77	35	16	47	175
% Female	48.73%	50.72%	34.78%	54.02%	48.61%
WRAT3 Arithmetic	57.82 (19.38)	13.62 (7.34)	50.26 (17.05)	10.34 (7.66)	36.84 (26.81)
WRAT3 Reading Mean Score	63.42 (19.24)	53.00 (17.18)	12.09 (8.20)	10.14 (8.11)	41.99 (28.77)
Estimated IQ	109.61 (14.28)	97.75 (13.45)	100.80 (16.42)	88.74 (13.57)	101.17 (16.51)
Education Rating	3.23 (1.41)	3.10 (1.29)	2.43 (1.19)	3.15 (1.22)	3.08 (1.31)
Occupation Rating	3.65 (1.30)	3.69 (1.53)	3.67 (0.87)	3.20 (1.44)	3.54 (1.37)
Median Incomea Rating	3.52 (1.63)	3.41 (1.36)	3.50 (1.34)	3.49 (1.28)	3.49 (1.48)

Numbers in parentheses represent one standard deviation from the mean. Underlined numbers are mean percentile scores that are within the 25th percentile on the WRAT3 Arithmetic or WRAT3 Reading subtest.

The distribution of income relative to the period studied was relatively stable in Vancouver and comparable to other big cities (>1M) in Canada (i.e., Toronto, Montreal, Ottawa, Calgary). Although the distribution was positively skewed around the mean (60–70 K in CND$) relative to other cities, because we do not intend to generalize our results to the entire Canada, what is most relevant is that there were no differences in income distribution between our four groups.

Other MANOVA and MANCOVA analysis using a similar approach as the one used to investigate age effects showed no significant sex differences.

Diagnostic Criteria and Subgroups

Using previously established cut-off criteria (15, 100), each participant was assigned to one of four groups: the math disability (MD) group (participants who scored 25th percentile or lower on the WRAT3 Arithmetic subscale), the reading disability (RD) group (25th percentile or lower on the WRAT3 Arithmetic subscale), the comorbid math and reading disability (MDRD) group (25th percentile or lower on both WRAT3 subscales), and the typical achievement (TA) control group (higher than the 25th percentile on both WRAT3 subscales). The mean percentile scores for each group and mean age of the participants (on the day of testing) are reported in Table 1.

Three measures of socioeconomic status (SES)—education, occupation, and median income—were evaluated in the present study (reported in Table 1). To measure SES, we used the Kuppuswamy's socioeconomic ranking scale validated for urban communities (101). Each participant received a numerical rating between 1 and 7 for the highest level of education they had achieved by the day of testing (1 = elementary school certificate or currently enrolled, 2 = middle school certificate, 3 = secondary school diploma, 4 = some college/university or post-secondary diploma, 5 = college/university degree, 6 = graduate degree, 7 = professional degree). Each participant received an individual rating for their occupation status (1 = unemployed, 2 = unskilled worker, 3 = semi-skilled worker, 4 = skilled worker, 5 = clerical, shop-owner, farmer, 6 = semi-profession, 7 = profession). For the participants ages 16 and younger, the highest level of occupation status achieved by either one of their parents was used as a proxy for their own occupation rating. Median household income ratings were generated for each participant by the postal code of the home address that they provided on the day of testing (1 = $50,000 or less, 2 = $50,000–$60,000, 3 = $60,000–$70,000, 4 = $70,000–$80,000, 5 = $80,000–$90,000, 6 = $90,000 or more), based on most temporally proximal Canadian population census data (102). Preliminary one-way ANOVAs and Tukey post-hoc multiple comparisons were conducted to identify any between-group differences in the three SES measures. There were no significant between-group differences in occupation or median income rating. There was only a significant difference in mean education rating between the TA and RD groups (p = 0.007); as previously noted, this is explained by the lower average age of the RD group, when the contrast on mean education rating was run controlling for age this difference was no longer significant.

Statistical Analysis

The analysis involved two separate sets of tests for the first and second set of hypotheses (see section The Present Study: Research Questions, Design and Hypotheses). Relative to the first set of hypotheses, a two-way ANOVA was conducted for each of the seven psychoeducational tests, to assess just the interaction between math disability and reading disability across the four groups. The model followed a 2 × 2 factorial design, where the two between-subject factors were math disability (with two levels, math disability vs. no math disability) and reading disability (also two levels, reading disability vs. no reading disability). We followed the same procedures consolidated in previous studies on individuals with comorbid math and reading disabilities (74, 90) whereby, the interaction term serves as an indicator of the type of relationship between deficits in individuals with MDRD. A significant 2-way interaction between math disability and reading disability would indicate a synergistic or antagonistic relationship between math and reading disability—an over-additivity or under-additivity of deficits in domain-specific or domain-general cognitive processes. Main effects were irrelevant to the objectives of the study and are not considered, to avoid redundancy. Nonetheless, for rigor, they were counted in the correction for Type I error inflation due to multiple testing (see below). To assess the second set of hypotheses, one-way ANOVAs and post-hoc Tukey pairwise comparisons were used to analyze focused between-group differences in performance between TA, RD, MD, and MDRD groups, with each psychoeducational test measurement as dependent variable, and learning disability groups as levels of the independent variable/factor. This followed directly from the second set of hypotheses for this study.

To counteract Type I error inflation, we adopted the standard Bonferroni criterion; effects were deemed significant if p was below 0.00192; this corresponded to the p-value adjustment: 0.05/26 tests, which included all interaction and main effect tests of the two-way ANOVA as well as all one-way ANOVAs. Correspondingly, for the Tukey procedure, the same correction was applied to keep p-values below adjusted 0.05 level.

Systematic Review of the Neuroanatomical Correlates of Dyslexia-Dyscalculia Comorbidity

The protocol for this review followed the guidelines established by the Preferred Reporting Items for systematic Review and Meta-Analysis Protocols (103); a detailed checklist with inclusion/exclusion criteria, search terms, and methods is presented in Table 2.

Table 2

PRISMA-P Protocol for Systematic Review (103).

Rationale	To identify any neuroanatomical structures whose atypical function may be associated with the cognitive deficits exhibited by individuals with dyslexia and dyscalculia
Objectives	The review answered the following questions: • “What brain regions show atypical activity in dyslexia alone?” • “What brain regions are show atypical activity in dyscalculia alone?” • “What brain regions are atypical activity in comorbid dyslexia-dyscalculia?
Eligibility criteria	Studies published in academic research journals since January 1, 2004. This marks the beginning of the current definition of specific learning disability (104) •The studies involved 20+ participants, males and females ages 6 and older •The studies followed a quasi-experimental design with at least two groups: one group with a learning disability (dyslexia or dyscalculia) and a control group •The studies did not involve individuals with any medical condition (other than dyslexia or dyscalculia) or any other life circumstance that could have influenced their performance on the cognitive tasks (ADHD, neurodegenerative disease, lack of education, etc.) •The investigators applied one of the three following techniques:   a) Functional magnetic resonance imaging (fMRI) to examine physiological correlates of cognitive activity during phonological or numerical magnitude comparison tasks   b) Diffusion tensor imaging (DTI) to examine structural differences in white or gray matter composition between key neurological structures   c) Lesion-symptom mapping (caused by either a stroke or a brain tumor)
Information sources	•Google Scholar •American Psychological Association (PsycINFO) •Education Resources Information Center (ERIC) •NIH MEDLINE Database (PubMed) •Web of Science
Search strategy	Step 1: Preliminary Search A preliminary search was performed using Google Scholar to find the leading authors in learning disabilities research, identify their seminal publications on dyslexia, dyscalculia, and provide a working definition for each disorder Step 2: Existing Meta-Analyses A secondary search was performed to using PsycINFO and ERIC to identify studies that examined comorbid dyslexia-dyscalculia, and to identify any existing meta-analyses on the cognitive or neurological correlates of each learning disability Step 3: Detailed Search A detailed search of medical literature was performed using PubMed and Web of Science to identify empirical studies that used functional or structural MRI to examine individuals with (i) dyscalculia and (ii) dyslexia, and that report the neuroanatomical structures where atypical function, white matter composition, or functional connectivity is associated with each disorder   a) A combination of the following search terms were used to identify functional and structural neuroimaging studies on dyslexia: “neurobiological dyslexia” “neurobiological reading disability” “brain region dyslexia” “brain region reading disability” “neuroimaging dyslexia” “neuroimaging reading disability” “fMRI dyslexia” “fMRI reading disability” “MRI dyslexia” “MRI reading disability” “DTI dyslexia” “DTI reading disability”   b) A combination of the following search terms were used to identify functional and structural neuroimaging studies on dyscalculia: “neurobiological dyscalculia” “neurobiological math disability” “brain region dyscalculia” “brain region math disability” “neuroimaging dyscalculia” “neuroimaging math disability” “fMRI dyscalculia” “fMRI math disability” “MRI dyscalculia” “MRI math disability” “DTI dyscalculia” “DTI math disability” Step 4: Lesion-symptom Mapping Studies A second detailed search of medical literature was performed using PubMed and Web of Science to identify empirical studies that examined patients with alexia (acquired dyslexia) or acalculia (acquired dyscalculia), who had suffered damage to structures that mediate in math or reading ability, caused by an ischemic stroke or brain tumor   a) A combination of the following search terms were used to identify lesion-symptom case studies of patients with alexia: “neurobiology AND acquired dyslexia” “neurobiology AND alexia” “lesion AND acquired dyslexia” “lesion AND alexia” “stroke AND acquired dyslexia” “stroke AND alexia” “brain tumor AND acquired dyslexia” “brain tumor AND alexia”   b) A combination of the following search terms for acquired dyscalculia: “neurobiology AND acquired dyscalculia” “neurobiology AND acalculia” “lesion AND acquired dyscalculia” “lesion AND acalculia” “stroke AND acquired dyscalculia” “stroke AND acalculia” “brain tumor AND acquired dyscalculia” “brain tumor AND acalculia”
Study records	One independent reviewer selected the published studies that fit the eligibility criteria. The selected publications were legally stored and classified using Mendeley Desktop (Version 1.15.2) for Windows 10
Outcomes and prioritization	The desired outcome was a list of brain regions that are involved in dyslexia and dyscalculia. Priority was given to studies that included participants all four groups (participants with dyslexia, dyscalculia, comorbid dyslexia-dyscalculia, and controls)
Synthesis	The results of the systematic review are synthesized using a table as displayed below. Each brain region identified in the review is classified by learning disability (whether the region is associated with dyslexia, dyscalculia), as well as by the type of atypical functionality displayed (whether the brain region is generally more active or inactive in individuals with the learning disability). Of primary interest are the neuroanatomical structures whose atypical function is common to dyslexia and dyscalculia; these structures are underlined in the table

The systematic review was performed in four stages. The first stage was a preliminary search to identify well-cited authors on math and reading disabilities and the avenues for future research using Google Scholar. The second stage of review provided working definitions of dyslexia and dyscalculia using PsycINFO and the Education Resources Information Center (ERIC). The third stage served to identify the neural correlates unique to dyslexia, the neural correlates unique to dyscalculia, and the neural correlates that are shared between the disorders. A detailed search of biomedical literature was performed to examine evidence from four types of studies: (i) functional neuroimaging studies, (ii) structural neuroimaging studies, (iii) functional connectivity studies, and (iv) lesion-symptom mapping studies. This stage of the review was conducted using literature available through the Web of Science and the National Center for Biotechnology Information (PubMed). The fourth and final stage of the review was initially conducted in April 2015 and identified 26 empirical studies met inclusion criteria. This stage of the review was repeated in April 2020 to add updated findings from the literature; 24 additional empirical studies were identified.

The functional neuroimaging studies included compared the brain physiology of people with and without dyslexia during reading tasks or compared the brain physiology of people with and without dyscalculia while they performed mathematical tasks. The structural neuroimaging compared the white and/or gray matter volume in specific neuroanatomic regions among people with dyslexia and/or among people with dyscalculia and normal controls. Similarly, the functional connectivity studies used the same types of comparisons applying MRI tractography in multiple neuroanatomic regions. To support the model with direct evidence, the searches also identified empirical clinical studies that examined patients with alexia (acquired dyslexia) or acalculia following traumatic brain injury.

Results

Tests of Phonological Processing

Mean scores on the tests of phonological processing (Rosner Auditory Analysis: RAA, Pseudowords: PW, Word Attack: WA) are shown in the panels of Figure 2. The results of the two-way ANOVA did not reveal a significant interaction between math disability and reading disability for any of these tasks (all F's < 2.89; p > 0.10, ≤ 0.01). The one-way ANOVA identified a significant effect of learning disability group on the mean scores of the RAA [F(3, 356) = 28.90, MSE = 2448.46, p < 0.0001, η2 = 0.196], PW [F(3, 356) = 39.28, MSE = 2067.86, p < 0.0001, η2 = 0.25], and WA [F(3, 356) = 49.92, MSE = 4528.74, p < 0.0001, η2 = 0.30]. For all three tasks, Tukey post-hoc multiple pairwise comparisons revealed a similar pattern of significant intergroup mean scores differences, showing TA > RD, and TA > MDRD (all p's < 0.01). For all other comparisons, RD = MD = MDRD (For more details see Note in Figure 2).

Figure 2

Top Panel: Mean scores for the Rosner Auditory Analysis task. Middle Panel: Mean scores for the Pseudowords Phoneme Deletion task Bottom Panel: Mean scores for the WRMT-R Word Attack subtest. For all panels, bars represent one standard errors. Asterisks summarize the results of post hoc Tukey test comparisons: “*” indicates a significant difference from the typical achievement (TA) control group at p < 0.05; “**” indicates a significant difference from the reading disability (RD) group at p < 0.05. TA > RD (Rosner Auditory Analysis: 8.29 [CI: 4.31 to 12.27]; Pseudowords: 6.15 [CI: 3.02 to 9.29]; Word Attack: 12.65 [CI: 8.53 to 16.76]; all p's < 0.01). TA > MDRD (Rosner Auditory Analysis: 7.30 [CI: 7.30 to 13.64]; Pseudowords: 9.84 [CI: 7.34 to 12.43]; Word Attack: 12.56 [CI: 9.27 to 15.84]; all p's < 0.01). For all other comparisons, RD = MD = MDRD.

The KeyMath Interpreting Data Subtest (Henceforth, KeyMath-ID)

Mean scores and standard errors for the KeyMath-ID are shown in Figure 3. The two-way ANOVA did not reveal a significant interaction between math disability and reading disability (F < 1, p = 0.73, η2 = 0.01). The one-way ANOVA identified a significant effect of learning disability group on the level of performance [F(3, 356) = 12.23, MSE = 238.550, p = 0.0001, η2 = 0.093]. Tukey comparisons revealed that the mean scores of the TA group were significantly higher than all other disability groups (TA> MD, TA > RD, and TA > MDRD (all p's < 0.05). However, there were no other significant differences among the mean scores of the groups of individuals with disabilities, that is, RD = MD = MDRD (For more details see Note in Figure 3).

Figure 3

Mean scores for the KeyMath Interpreting Data subtest. Bars represent one standard errors. The symbol “*” indicates a significant difference from the typical achievement (TA) control group at p < 0.05 on post hoc Tukey test. The symbol “#” indicates a marginally significant trend at 0.05

MD (1.72, [CI: 0.07 to 3.36], p = 0.04). TA > RD (2.16, [CI: 0.25 to 4.07], p = 0.02). TA > MDRD (3.47, [CI: 1.95 to 4.99], p < 0.01). RD = MD. RD = MDRD. # MD > MDRD (1.75, [CI: −0.08 to 3.59], p = 0.068).

Mean scores for the KeyMath Interpreting Data subtest. Bars represent one standard errors. The symbol “*” indicates a significant difference from the typical achievement (TA) control group at p < 0.05 on post hoc Tukey test. The symbol “#” indicates a marginally significant trend at 0.05