A Neuropsychological Profile of Developmental Dyscalculia: The Role of Comorbidity

Abstract

Developmental dyscalculia (DD) has long been thought to be determined by multiple components. Dyscalculia has high comorbidity with other learning and developmental disabilities, including reading and writing disorders, attention deficits, and problems in visual/spatial skills, short memory, and working memory. This study aims to assess prevalence rates for isolated as well as comorbid DD in a sample of Italian-speaking children. In addition, we studied the neuropsychological profile of children with isolated or combined dyscalculia. We tested 380 children (176 males and 204 females) between the ages of 8.17 and 9.33 years using an extensive battery to determine the neuropsychological profile. The assessment included an arithmetic battery and nonverbal intelligence, short-term memory, reading, and writing tests. The results indicated that children with DD more frequently have a reading disorder and writing disorder. They also have a lower nonverbal intelligence quotient (IQ) and obtain significantly lower scores in short-term memory tests and on a visuospatial skills questionnaire. They also had significantly higher scores (indicative of greater attentional difficulties) on the Conners subscale for attentional problems. Children with DD present different cognitive and neuropsychological profiles.

Keywords

dyscalculia assessment comorbidity neuropsychological profile

Developmental dyscalculia (DD) is a learning disability (LD) with a persistent impairment in mathematics: Children with DD might display deficits in number sense, memorization of arithmetical facts, accurate or fluent calculation, and math reasoning (Diagnostic and Statistical Manual of Mental Disorders–Fifth Edition [DSM-5; American Psychiatric Association [APA], 2013]). In Western countries, children with DD have been shown to be about 3% to 7% of the school-age population (Butterworth, 2005; Lewis et al., 1994; Reigosa-Crespo et al., 2012; Shalev et al., 2000; von Aster & Shalev, 2007). Dyscalculia also appears to have high comorbidity with reading and/or spelling disorders (Dirks et al., 2008; Lewis et al., 1994; Moll et al., 2014; Ramaa & Gowramma, 2002; von Aster & Shalev, 2007) as well as with attention-deficit/hyperactivity disorder (ADHD; Kuhn et al., 2006; Monteaux et al., 2005; von Aster & Shalev, 2007). Moreover, numerous studies found that children with DD have cognitive impairments in short-term and working memory (Bull et al., 2008; Geary, Hoard, Nugent, et al., 2012; Mammarella et al., 2015; Passolunghi & Siegel, 2004; Raghubar et al., 2010; Szűcs, 2016) and visual-spatial proficiency (Andersson & Lyxell, 2007; D’Amico & Guarnera, 2005; Geary, 1993; Mammarella & Cornoldi, 2005; Szűcs, 2016). Because many factors can influence the prognosis of DD and academic success, it is important to investigate the cognitive and neuropsychological profile to implement appropriate educational interventions and to reduce the negative impact on the school and social–emotional well-being.

Developmental Dyscalculia

Developmental dyscalculia concerns the normal acquisition of arithmetic skills with a negative impact in many everyday contexts and on occupational achievement in adult life (Parsons & Bynner, 1997; Rivera-Batiz, 1992). Moreover, children and adolescents with DD develop diverse mental health problems (Schulte-Körne, 2016; Willcutt et al., 2013). The prevalence of DD depends on the definition of dyscalculia, the criteria used to diagnose it, and the efficacy of educational programs and instructional methods (Shalev, 2007). Ramaa and Gowramma (2002) noticed that out of a sample of 251 students in Indian primary schools Grades 3 and 4, 6% had an isolated form of DD. In the United States, Barbaresi et al. (2005) observed different prevalence rates of DD’s incidence using different analysis criteria: incidence of DD in children up to 19 years was 5.9%, 9.8%, and 13.8%. In Belgium, dyscalculia was diagnosed in 2.3% of second graders, 7.7% of third graders, and 6.6% of fourth graders (Desoete et al., 2004). An English survey (Lewis et al., 1994) identified the same prevalence rate: 2.3% of children with dyscalculia (n = 1,056, 9- to 10-year-old students in Grade 5, urban and rural primary school; low-achievement formula). Regarding the relationship between dyscalculia and gender ratio, the literature indicated higher prevalence rates of learning disabilities for boys versus girls (Badian, 1999; Barbaresi et al., 2005; Landerl & Moll, 2010; Ramaa & Gowramma, 2002; Rutter et al., 2004); nevertheless, some surveys reported higher rates among females (Landerl & Moll, 2010; Moll et al., 2014), while other findings observed balanced gender ratios (Devine et al., 2013; Dirks et al., 2008; Gross-Tsur et al., 1996; Lewis et al., 1994; Mazzocco & Myers, 2003).

Very few studies have been conducted on hypothetical links between dyscalculia and socioeconomic status (SES), and their scope has been limited to generic learning difficulties regarding mathematics. In a neuroimaging study, Demir et al. (2015) demonstrated that the neural bases of numerical cognition systematically vary according to SES and children’s simultaneous mathematical skill level. Moreover, during the preschool years, children exhibit a very specific tendency: Garon-Carrier et al. (2018) found that children with low numerical knowledge skills came from low-income families—and their fathers had a low educational level. The children showed limited early cognitive development—and revealed lower scores in memorization and visual/spatial capabilities. The limited early cognitive development and the lower score in memorization and visual/spatial capabilities are referred to children.

Comorbidity

Developmental dyscalculia is usually associated with other LDs, such as dyslexia and/or writing disorders. In 2007, von Aster and Shalev observed that only one-third of children with DD exhibited pure dyscalculia (without any other comorbid LDs). Lewis and colleagues (1994) found dyscalculia to be an isolated learning disorder in 1.3% of a group of 9- and 10-year-old British schoolchildren (N = 1,056). Moll et al. (2014) found that comorbid learning disorders were significantly more frequent than isolated disorders, but only if they used the criterion of 1 standard deviation (this criterion actually raised the rates of comorbidity as it included more participants in the group of children with learning problems). On the contrary, considering the association of the three learning domains, Ramaa and Gowramma (2002), who were studying 78 children with dyscalculia, discovered that 51% of them also presented with reading and writing problems. Thirty percent showed only dyscalculia, while 18% had a diagnosis of mathematical and writing problems. Lewis et al. (1994) found that 24 of 1,206 British schoolchildren (2.3%) displayed reading and mathematical disorders, whereas about 7 of 15 Greek schoolchildren with low calculation obtained low reading scores (Koumoula et al., 2004). By 2008, the prevalence of comorbidity between reading and arithmetic disabilities (7.6%) was greater than expected based on the prevalence of a specific disability in reading or arithmetic alone; moreover, the comorbidity group had more generalized achievement difficulties than the single-impairment groups (Dirks et al., 2008). Willcutt and colleagues (2013) found that comorbidity between dyslexia and dyscalculia was associated with difficulties in verbal comprehension, working memory, and processing speed; Peterson et al. (2017) observed that processing speed contributes to the overlap between reading and attention as well as math and attention; furthermore, verbal comprehension contributes to the overlap between reading and math. In some cases, children with dyscalculia also showed attentional impairments (von Aster & Shalev, 2007); mathematical disability and attention disorders had a high rate of co-occurrence (Fletcher, 2005; Zentall, 2007). Kuhn et al. (2016), who compared children with DD, with/without ADHD, with isolated ADHD, and a control group, observed that children with mathematical difficulties also had significantly lower scores on sustained attention tests, including working memory tests. DuPaul et al. (2013) additionally stated that comorbidity levels involving DD and ADHD can fluctuate between 5% and 30%.

Cognitive Factors and Dyscalculia

Children with dyscalculia can have different neuropsychological profiles, which can delay the typical acquisition of mathematical skills; they can also show difficulties with cognitive factors, such as working memory, inhibition, and visuospatial issues (Geary, 2004; Menon, 2016; Passolunghi & Siegel, 2001, 2004; von Aster & Shalev, 2007).

Many researchers have repeatedly demonstrated the importance of working memory for mathematical achievement in typically developing (TD) children (Alloway & Passolunghi, 2011; Berg, 2008; Bull et al., 2008 for a review, see Raghubar et al., 2010). Working memory is the ability to store information while doing other cognitively draining tasks (Gathercole et al., 2006), and many studies have shown that poor working memory is a primary cognitive signature of dyscalculia (Allen et al., 2020; Andersson, 2008; Swanson & Sachse-Lee, 2001). Children with dyscalculia have displayed difficulty in all components of Baddeley’s working memory model (Baddeley, 2012; Bull et al., 2008; Geary, Hoard, Nugent, et al., 2012). The phonological loop supports learning the verbal number sequence and fact retrieval, and it is an important predictor of performance in single-digit addition and subtraction problems (Hecht, 2002; Seyler et al., 2003). In general, children with dyscalculia could present poor performance on working memory tasks in addition to significantly compromised results in specific and diagnostic mathematical tests, such as in calculation and/or word problem-solving tasks. Passolunghi and Siegel (2001, 2004) noted that fourth graders with dyscalculia had smaller digit spans than typically achieving peers, which may impair arithmetic performance. They also noticed deficits in both number and letter- or word-recall tasks, indicating a general impairment of verbal working memory in children with dyscalculia. Other researchers have found that visual spatial memory supports number representation, counting, arithmetic tasks, word problem solving, and geometry (Ashkenazi et al., 2013; Caviola et al., 2012; Giofrè et al., 2013; Swanson & Sachse-Lee, 2001; Zheng et al., 2011). Several studies showed that visuospatial memory is implicated in mathematical difficulties (Andersson & Lyxell, 2007; D’Amico & Guarnera, 2005; McLean & Hitch, 1999; Passolunghi & Cornoldi, 2008; Szűcs et al., 2013). In particular, Geary et al. (2007) and Kyttälä et al. (2010) found deficits on such tasks as maze memory and location recall among children with dyscalculia. Moreover, the central executive has been correlated with sophisticated strategies for solving problems (Geary, Hoard, & Nugent, 2012) and general mathematics ability (Mazzocco & Kover, 2007). Geary (1993) identified a visuospatial subtype as a frequent profile for children with DD: DD was associated with impaired visual-spatial processing abilities, and the children showed difficulties in spatially representing numerical information. Visual-spatial deficits have been attributed to children with poor mathematical achievement as well (Karagiannakis et al., 2014; Mammarella & Cornoldi, 2005; Szűcs, 2016).

Present Study Purpose and Research Questions

This study aimed to assess prevalence rates for isolated as well as comorbid DD in a sample of Italian-speaking children. We also investigated the children’s cognitive and neuropsychological profiles.

To address this purpose, we posed the following questions:

Research Question 1 (RQ1): What are the prevalence rates of DD and isolated as well as comorbid DD?

Research Question 2 (RQ2): What are the significant socio-demographic factors of children classified with DD?

Research Question 3 (RQ3): Are there differences in the cognitive factors and the neuropsychological profiles of children with dyscalculia (isolated or combined) and without DD?

Method

Participants

The Child Neuropsychiatry Unit in the Department of Experimental Medicine at the University of Insubria promoted a program of early identification and treatment of learning disabilities (regarding reading and writing screening and activities to enhance teachers’ reading and writing instruction), and in this context, we met the parents and teachers of 407 children attending the third grade in 13 primary schools in the northern Italian city of Varese. We explained the aims of the study to obtain their informed consent (which had the local ethics committee’s approval) for the children’s participation. We also used an anamnestic questionnaire to collect information about the child and the family’s SES, including professional qualifications and years of education of the parents, age of the parents, siblings (yes or no), age of siblings, kindergarten experience (yes or no), and relatives with a diagnosis of some neurodevelopmental or psychiatric disorders. Children with intellectual disabilities or neurological or psychiatric disorders (e.g., an autism spectrum disorder, cerebral palsy [n = 19]) or without parents’ informed consent (n = 8) were excluded from the study. In total, we collected data from 380 children (176 males and 204 females), between the ages of 8.17 and 9.33 years (M = 8.8 ± SD = 0.28 years). All our participants were native Italian speakers and were Caucasian (n = 368), Asian (n = 1), African (n = 4), or Arab (n = 7). In regard to the overall SES, our sample included 204 (53.7%) mothers and 124 (32.6%) fathers who had a low level of education. Children were included if they met the following criteria: (a) they spoke Italian as their first language; (b) they did not have any indication of major cerebral damage, congenital malformations, and neurological, visual, or hearing impairments; (c) they did not have any indication of intellectual disabilities; and (d) they received adequate schooling (i.e., regular school attendance). Some of these criteria (e.g., children with an intelligence quotient (IQ) less than 70 or other known neurological or psychiatric disorders; regular school attendance) were chosen from indicators of the International Classification of Diseases–10th Revision (ICD-10; World Health Organization, 1992) and criteria from the 2007 Italian Consensus Conference; the presence of these criteria excluded the diagnosis of LD.

Measures

The assessments covered nonverbal IQ, short-term memory, attention and visuospatial abilities, mathematical abilities, reading (text, word, pseudo-word), writing (dictations of word and pseudo-words), and text comprehension. All the tests have been standardized on the Italian language and validated, and they provided normative data for the considered age group.

Nonverbal IQ

We administered the Colored version of the Raven Progressive Matrices to evaluate the general intelligence of the children (CPM; Raven, 1994; Italian version: Belacchi et al., 2008). Raven’s CPM are common measures of basic cognitive functioning, quantifying a child’s ability to form perceptual relations and to reason by analogy, independent of verbal abilities and formal schooling. It is a non-verbal IQ test, and each of the 36 test items consists of an incomplete abstract pattern. Participants are required to select, from a set of six, the figure needed to complete the pattern correctly. The raw scores were converted into z-points with reference to Italian normative data; thereafter, the z-points were converted into IQ scores. The test’s reliability is about 0.90.

Verbal short-term memory

We administered two verbal tests from the Neuropsychological Evaluation Battery for the Developmental Age (BVN 5-11; Bisiacchi et al., 2005): digit span forward and verbal span forward. This test’s reliability is about 0.84.

Visuospatial memory

The Corsi Block-Tapping Task (Corsi, 1972; Italian version: Mammarella et al., 2008) is a widely used paradigm to assess short-term and working memory using a nonverbal task. The task consists of a board containing nine cubes at fixed, pseudo-random positions. Children have to reproduce the same sequence in increasing length. Items are presented at a rate of one cube per second. The test–retest reliability (on 35 participants re-tested after 2 weeks) is 0.38 (Spinnler & Tognoni, 1987).

ADHD symptoms

The parent (CPRS-R:S) and teacher (CTRS-R:S) versions of the Conners’ Rating Scales–Revised (Conners, 2000; Italian version: Nobile et al., 2007) consist of 27 items in the parent version and 29 in the teacher version. Each item matches a specific frequent behavioral pattern of children with ADHD, to be valued on a scale of 0 to 3 points. For this study, we selected and analyzed the “Inattention content scale” to detect the presence of decreases in attention and increases in distractibility. We converted the raw scores into T-scores with reference to Italian normative data. Internal consistency reliability (Cronbach’s α) was good, ranging from 0.745 (Hyperactivity subscale) to 0.897 (ADHD index) for the CPRS-R:S and from 0.847 (Oppositional subscale) to 0.924 (ADHD index) for the CTRS-R:S.

Visuospatial abilities

The Shortened VisuoSpatial Questionnaire (SVQ; Cornoldi et al., 2003) is a short screening questionnaire addressed to teachers. It allows examiners to identify children lacking nonverbal skills and to look into some aspects of visuospatial capabilities, which can interfere with school learning. It consists of 18 items. For each item, the teacher makes a judgment based on the frequency of the behavior or skill under consideration, choosing among four frequency options: never or seldom (1), sometimes (2), often (3), and very often or always (4). The test’s reliability ranges between 0.90 and 0.95.

Mathematical abilities

For math abilities, we used the Developmental Dyscalculia Battery (BDE; Biancardi & Nicoletti, 2004), which consists of several subtests from which accuracy and/or total time can be determined. For diagnostic purposes, we grouped the subtests’ scores into two main subquotients, the Numerical Quotient (NQ) and the Calculation Quotient (CQ). The NQ was based on the average scores from the five subtests: accuracy and total time scores for counting, accuracy, and total time scores for Arabic number reading; accuracy score for Arabic number writing; accuracy score for number repetition; accuracy and total time scores for the magnitude representation. We based the CQ on the average scores from five subtests: accuracy scores for two items of facts retrieval; accuracy score for simple oral calculation; accuracy score for complex oral calculation; and accuracy score for written calculation. Finally, the BDE generates a Number and Calculation Quotient (NCQ), a global score useful for the diagnosis of DD. The scores from these two quotients were then age-standardized. Internal consistency reliability (Cronbach’s α) was good, ranging from 0.83 (numerical tasks accuracy) to 0.76 (numerical task fluency) and 0.72 (calculation tasks).

Reading, writing, and text comprehension

The Battery for the Assessment of Developmental Dyslexia and Spelling Disorders (DDE-2; Sartori et al., 2007) was used to assess students’ reading (word and nonword) and writing. This tool is a widely used diagnostic test in Italy. The subtests selected for this study were Word Reading and Nonword Reading and Word/Nonword Dictation. The child is asked to read a list of words for the Word Reading subtest, and a list of nonwords for the Nonword Reading subtest. Each child is asked to read aloud as quickly and accurately as possible. The procedure requires the examiner to time the performance and make notes of the mistakes without interrupting the child. For each subtest, we scored the time (in seconds) and the number of incorrect pronunciations (errors) in reading the lists. For the Word Dictation subtest, the child must write a list of words, and for the Nonword Dictation subtest, a list of nonwords. Each child must write as accurately as possible. The procedure requires the examiner to make note of the mistakes. The reliability is 0.77 for fluency and 0.56 for accuracy.

The MT Reading Text (Cornoldi & Colpo, 1998, 2012) is a psychometrically valid Italian instrument that measures oral reading speed and accuracy. It consists of a series of texts for each grade level. The child is asked to read aloud as quickly and accurately as possible the text chosen according to his or her school grade level. During the test, the examiner times the reading and makes notes of the mistakes. We scored the number of syllables per second (speed) and the number of misread words (errors) in reading the text. The reliability of the test ranges from 0.752 to 0.869 for accuracy and from 0.943 to 0.967 for fluency. The text comprehension section (Cornoldi & Colpo, 1998, 2012) was administered exactly followed the standard procedure used by all the Italian standardized reading comprehension tasks. Participants had to silently read one passage and answer 10 questions related to the text. The test conductors gave them unlimited time to complete the task, assured them that the time was not considered in any way in the final evaluations, and allowed them to consult the text. The total score consists of all correct answers to the questions. The reliability of the tests ranges from 0.573 to 0.700.

Procedures

Over a 2-month period, every child participant was placed in a room set apart from the class during school hours. Over a total of two 45-min sessions, a child neuropsychiatrist and/or a psychologist (trained on learning disabilities in a university master’s course) administered an extensive battery of standardized neuropsychological tests. We coded the data gathered from the anamnestic questionnaire as qualitative variables. We also coded the data gathered from the tests as quantitative variables, and the normal range of performance was established with reference to available Italian normative data.

We recorded accuracy and total time for mathematical tasks. Based on the Consensus Conference (2007) criteria, a child was diagnosed as having DD if their NCQ was at least 2 SD below the average (i.e., children with NCQ < 70 were categorized as DD). We recorded accuracy and total time for each reading task (text, word, pseudo-word). Also based on the Consensus Conference (2007) criteria, which were promoted by the Italian National Institute of Health, (Lorusso et al., 2014), participants were defined as having dyslexia if they scored below the 5th percentile (for accuracy) or at least 1.5 SD below the mean (for fluency) in at least three of the six scores. There is still no agreement on the diagnosis of reading comprehension in Italy. Currently, the diagnosis of dyslexia does not require a deficit in reading comprehension, but it is common to find it (Panel D.A.E.R.D. Consensus Conference DSA, 2011). We recorded accuracy for writing tasks (dictations of words and pseudo-words), and we taking into account the Consensus Conference (2007) criteria, we defined children as having a writing disorder if they had a lower performance (<5th percentile) on at least one of the two tests.

Statistical Analyses

We performed the statistical analysis of the data using SPSS 16.0 software. Prior to conducting analyses, the data were checked for violation of assumptions of normality and homogeneity of variance using the Kolmorogov–Smirnov and Levene tests, respectively. We employed a t test (for continuous variables) or a chi-square test (or Fisher’s exact test when appropriate, for categoric variables) to compare the TD group and the DD group. This comparison was carried out on all variables: sociodemographic characteristics; results of neuropsychological tests; and scores on IQ, reading and writing, attention problems, and arithmetic tests. Logistic regression was used for multivariate modeling. We performed such analysis on all variables listed above in order to discern which ones were mathematical ability predictive factors. We used the one-way analysis of variance (ANOVA) for independent variables to compare the quantitative variables (the mean scores on the neuropsychological tests and questionnaires) of the groups (including multiple comparisons using Scheffés’ statistic). We also performed a correlation analysis (r² of Pearson’s correlation coefficient) of the scores obtained on the attention and visuospatial-related questionnaires and those obtained from arithmetic tests for all the study groups.

Results

Research Question 1

In the examined cohort, 37 of 380 (9.7%) children presented with DD, and the boys to girls ratio was 1.2:1. Among these 37 children, 62.2% (6.1% of the population) had reading and/or writing problems, and 37.8% had dyscalculia only (3.7%).

Research Question 2

Compared to their TD peers, children with DD more frequently had parents with a lower educational level (father: 74.3% vs 40.0%, mother: 58.3% vs 30.4%, p < 0.001); they more frequently exhibited reading-related learning impairments (35.1% vs 2.0%, p < 0.001); and writing-related learning impairments (51.4% vs 9.6%, p < 0.001).

Research Question 3

The descriptive data regarding the variables for the four groups are presented in Table 1. We performed a one-way comparison via ANOVA (using Scheffé’s test for multiple comparisons) between results obtained from the TD group (n = 306), the DD group (n = 14), the DD + LDs group (n = 23), and the Other LDs group (n = 37). The results by ANOVA indicated that except for two subscale scores (Nonverbal IQ and Verbal Short Memory Letters), the subscale scores were significantly different among the groups (all p < 0.05; see Table 2). There were no statistically significant differences between the groups in the Nonverbal IQ, F(3, 381) = 2.815, p = 0.039, effect size (η²) = 0.022;, and Verbal Short Memory Letters, F(3, 379) = 4.716, p = 0.003, η² = 0.036. We found a significant difference between the groups for the digit span task, F(3, 379) = 13.206, p < 0.001, η² = 0.095. The groups also were statistically different in their reading abilities in terms of fluency on the reading of the MT text, F(3, 383) = 36.873, p < 0.001, η² = 0.224,; the Word Reading subtest, F(3, 383) = 25.817, p < 0.001, η² = 0.168; and the Nonword Reading subtest, F(3, 383) = 14.906, p < 0.001, η² = 0.105. The groups were also statistically different in terms of reading accuracy on the MT text, F(3, 383) = 66.132, p < 0.001, η² = 0.341; Word Dictation subtest, F(3, 383) = 63.635, p < 0.001, η² = 0.333; and the Nonword Dictation subtest, F(3, 383) = 37.843, p < 0.001, η² = 0.229. Furthermore, the groups revealed significant differences on the reading comprehension tasks, F(3, 382) = 23.844, p < 0.001. Concerning their writing abilities, the groups were statistically different on the Word, F(3, 383) = 169.594, p < 0.001, η² = 0.571 and Nonword Writing, F(3, 382) = 27.708, p < 0.001, η² = 0.179, subtests. Regarding arithmetical skills, the groups were statistically different on the global quotient, F(3, 375) = 106.511, p < 0.001, η² = 0.537; NQ, F(3, 375) = 144.955, p < 0.001, η² = 0.460; and CQ, F(3, 375) = 97.488, p < 0.001, η² = 0.438. Finally, the groups differed significantly on visuospatial abilities, F(3, 372) = 46.238, p < 0.001, η² = 0.272, and attentional abilities, F(3, 382) = 79.654, p < 0.001, η² = 0.385.

Table 1.

Means and Standard Deviations for the Studied Variables by Group.

Variable	TD group^a		DD group^b		DD+LDs group^c		Other LDs group^d
Variable	M	SD	M	SD	M	SD	M	SD
Nonverbal IQ	108.02	8.80	102.67	7.89	104.97	7.31	108.83	9.46
Verbal short memory (digit span)	6.83	2.30	5.14	2.11	4.17	1.56	5.86	2.39
Verbal short memory (letters)	7.23	2.48	6.43	2.34	5.39	1.47	6.63	1.94
MT text reading (fluency)	3.35	0.89	2.61	0.91	1.76	0.65	2.38	0.72
MT text reading (accuracy)	3.67	2.69	5.82	2.01	13.41	7.91	6.95	4.54
MT text comprehension (errors)	1.68	1.49	3.14	1.88	4.22	2.15	2.76	1.98
Word reading (fluency)	2.70	0.80	2.19	0.80	1.52	0.61	1.95	0.62
Word reading (accuracy)	3.97	3.22	7.14	3.78	15.17	9.28	7.65	4.42
Nonword reading (fluency)	1.62	0.45	1.30	0.48	1.13	0.31	1.32	0.39
Nonword reading (accuracy)	5.71	4.23	10.57	4.24	15.17	6.53	8.05	4.89
Word writing (accuracy)	1.14	1.27	2.71	1.07	9.52	6.04	5.73	2.51
Nonword writing (accuracy)	2.18	1.76	3.50	1.02	5.22	3.57	4.28	2.11
BDE global quotient	103.65	12.79	58.86	6.02	58.64	6.94	95.38	11.87
BDE numerical quotient	106.15	11.62	67.71	11.61	71.18	12.12	100.16	10.76
BDE calculation quotient	99.97	13.90	61.14	10.92	58.27	8.89	91.55	13.70
Visuospatial abilities (SVQ)	34.48	5.18	23.83	6.98	22.57	5.88	30.54	7.26
Attentional abilities (teachers’s Conners)	48.22	7.12	64.01	13.09	70.86	10.36	55.68	8.62

Note. TD = typically developing; DD = developmental dyscalculia; DD + LDs = developmental dyscalculia + learning disorders (dyslexia and/or writing disorder); MT = mock test; Other LDs = LD with reading and/or writing disorders without developmental dyscalculia; BDE = Developmental Dyscalculia Battery (Biancardi & Nicoletti, 2004); SVQ = Shortened VisuoSpatial Questionnaire (Cornoldi et al., 2003 ).

n = 306. ^bn = 14. ^cn = 23. ^dn = 37.

Table 2.

One-Way Analysis of Variance in Group Comparisons.

Variable	ANOVA			Post-hoc analysis (p value)
Variable	F	p	η²	TD vs. DD	TD vs. DD + LDs	TD vs. Other LDs	DD vs. DD + LDs	DD vs. Other LDs	DD + LDs vs. Other LDs
No verbal IQ	2.815	0.039	0.022	0.154	0.411	0.964	0.898	0.155	0.395
Verbal short memory (digit span)	13.206	<0.001	0.095	0.059	<0.001	0.085	0.659	0.833	0.064
Verbal short memory (letters)	4.716	0.003	0.036	0.696	0.006	0.717	0.648	0.985	0.225
MT text reading (fluency)	36.873	<0.001	0.224	0.025	<0.001	<0.001	0.037	0.846	0.066
MT text reading (accuracy)	66.132	<0.001	0.341	0.151	<0.001	<0.001	<0.001	0.758	<0.001
MT text comprehension (errors)	23.844	<0.001	0.158	0.012	<0.001	0.002	0.269	0.882	0.007
Word reading (fluency)	25.817	<0.001	0.168	0.134	<0.001	<0.001	0.091	0.784	0.243
Word reading (accuracy)	63.635	<0.001	0.333	0.039	<0.001	<0.001	<0.001	0.989	<0.001
Nonword reading (fluency)	14.906	<0.001	0.105	0.073	<0.001	0.001	0.735	10.000	0.531
Nonword reading (accuracy)	37.843	<0.001	0.229	0.001	<0.001	0.014	0.027	0.413	<0.001
Word writing (accuracy)	169.594	<0.001	0.571	0.043	<0.001	<0.001	<0.001	<0.001	<0.001
Nonword writing (accuracy)	27.708	<0.001	0.179	0.108	<0.001	<0.001	0.078	0.755	0.239
BDE global quotient	106.511	<0.001	0.537	<0.001	<0.001	0.002	10.000	<0.001	<0.001
BDE numerical quotient	144.955	<0.001	0.460	<0.001	<0.001	0.033	0.857	<0.001	<0.001
BDE calculation quotient	97.488	<0.001	0.438	<0.001	<0.001	0.004	0.944	<0.001	<0.001
Visuospatial abilities (SVQ)	46.238	<0.001	0.272	<0.001	<0.001	<0.001	0.940	0.006	<0.001
Attentional abilities (teachers’s Conners)	79.654	<0.001	0.385	<0.001	<0.001	<0.001	0.082	0.006	<0.001

Note. Significant results are in bold. TD = typically developing; DD = developmental dyscalculia; DD + LDs = developmental dyscalculia + learning disorders (dyslexia and/or writing disorder); MT = mock test; Other LDs = LD with reading and/or writing disorders without developmental dyscalculia; BDE = Developmental Dyscalculia Battery (Biancardi & Nicoletti, 2004); SVQ = Shortened VisuoSpatial Questionnaire (Cornoldi et al., 2003 ).

The results indicated that DD children who also had other LDs registered lower performances across all skill levels compared to the other groups, while DD was paired with reading and writing performances within the norm, although they were significantly lower than those of the control (TD) group. In addition, the DD group displayed reduced visuospatial and attentional skills.

We used a logistic regression model to assess simultaneously the associations among nonverbal IQ, short-term memory, visuospatial difficulties, inattention, and writing and/or reading problems and class section (as the control variable), and the likelihood of DD at third grade. The results indicated that the groups did not differ as far as nonverbal intellect level and gender distribution, while several statistically relevant differences were encountered in reading and writing performance, attentional skills, and visuospatial capabilities.

Only two factors had an independent significant effect: the odds of DD at third grade increased by 1.14 (95% confidence interval (CI) [1.04, 1.26]; η² = 0.048) for a 1-point decrease on the SVG questionnaire and by 1.08 (95% CI [1.01, 1.16]; η² = 0.034) for a single-point increase on the Conners Scale attentional score.

Children with dyscalculia show lower nonverbal IQ (104.10 ± 7.51 vs 108.10 ± 8.86, p = 0.008, estimate = −4.01, 95% CI [−6.98, −1.03]), and their scores were significantly lower on the short-term memory tests and the visuospatial skills questionnaire when compared to the scores of the TD group. Compared to the TD group, children with dyscalculia prominently totaled higher scores (a clear indication of greater attentiveness issues) on the specific Conners subscale related to attentional problems (see Table 3).

Table 3.

Results of Statistical Comparison Between Children with Developmental Dyscalculia and Typical Development Children by Logistic Regression Level.

Variable	Children with DD^a	TD children^b	Univariate p	Estimates (ICD 95%)	Multivariate p	Multivariate OR (CI 95%)	Effect size
Male: % (n)	54.1 (20)	53.6 (184)	0.962		NS
Low maternal education: % (n)	58.3 (21)	30.4 (103)	0.001		NS
Low paternal education, % (n)	74.3 (26)	40.0 (134)	<0.001		NS
No verbal IQ, M (SD)	104.10 (7.51)	108.10 (8.86)	0.008	−4.01 (−6.98 to −1.03)	NS
Verbal short memory (digit span): M (SD)	4.54 (1.82)	6.72 (2.33)	<0.001	−2.18 (−296 to −1.41)	NS
Verbal short memory (letters): M (SD)	5.78 (1.89)	7.17 (2.44)	0.001	−1.38 (−2.19 to −0.57)	NS
Reading disorder at T3: % (n)	35.1 (13)	2.0 (7)	<0.001		NS
Writing disorder at T3: % (n)	51.4 (19)	9.6 (33)	<0.001		NS
Text comprehension, % (n)	27.0 (10)	2.9 (10)	<0.001		NS
Visuospatial abilities SVQ: M (SD)	23.03 (6.22)	34.05 (5.57)	<0.001	−11.02 (−13.04 to −9.01)	0.006	1.14 (1.04–1.26)	0.048
Attention level (teachers’s Conners): M (SD)	68.19 (11.81)	49.03 (7.64)	<0.001	12.01 (9.36–14.67)	0.029	1.08 (1.01–1.16)	0.034
Attention level (parent’s Conners): M (SD)	62.79 (12.04)	48.66 (8.44)	<0.001	10.87 (7.58–14.17)	0.051	1.05 (1.00–1.12)	0.026
Control variable					NS

Note. Significant results are in bold. DD = developmental dyscalculia; TD = typically developing; ICD = International Classification of Diseases.

n = 37. ^bn = 343.

Finally, a comparison of the collective BDE subtest scores for the 14 children diagnosed with isolated dyscalculia and the 23 children presenting comorbidity with one LD or more indicated that the two groups did not appear to be particularly dissimilar. Just two statistically relevant differences were found: children with isolated dyscalculia obtained considerably lower scores on the semantic coding test (6.79 ± 2.16 vs 8.41 ± 1.37; p = 0.021), and they also registered substantially higher scores on the number repetition test (9.43 ± 1.74 vs 8.18 ± 1.59; p = 0.034). The BDE quotients and subscales significantly correlated (r² of Pearson’s correlation coefficient) with the SVG and Conners’ questionnaire scores (see Table 4).

Table 4.

Pearson’s Correlations Between Developmental Dyscalculia Battery With SVQ and Conners’ Questionnaire Scores.

Variable	SVQ questionnaire		Conners questionnaire (teachers)
Variable	r ²	p	r ²	p
BDE
Global quotient	0.54	<0.001	−0.66	<0.001
Numerical quotient	0.43	<0.001	−0.58	<0.001
Calculation quotient	0.54	<0.001	−0.63	<0.001
BDE subtests
Counting	0.25	<0.001	−0.37	<0.001
Arabic number reading	0.35	<0.001	−0.48	<0.001
Arabic number writing	0.35	<0.001	−0.45	<0.001
Repetition	0.25	<0.001	−0.34	<0.001
Semantic area	0.38	<0.001	−0.43	<0.001
Multiplications	0.38	<0.001	−0.51	<0.001
Facts retrieval	0.42	<0.001	−0.48	<0.001
Calculation <10	0.37	<0.001	−0.41	<0.001
Calculation >10	0.47	<0.001	−0.52	<0.001
Written calculation	0.44	<0.001	−0.49	<0.001

Note. Significant results are in bold. SVQ = Shortened VisuoSpatial Questionnaire (Cornoldi et al., 2003 ); BDE = Developmental Dyscalculia Battery (Biancardi & Nicoletti, 2004).

Discussion

This study’s main goal was to look into DD prevalence as an isolated disorder and in comorbidity with other learning disorders. Our study group was composed of 380 primary school third-grade children. In addition, we investigated the cognitive and neuropsychological profiles of the study participants.

Prevalence

Different epidemiological studies carried out in various countries have estimated DD prevalence in the general school population to be from 3% to 7% (APA, 2013; Desoete et al., 2004; Ramaa & Gowramma, 2002; von Aster & Shalev, 2007). Our inquiry detected a slightly heightened disorder prevalence of about 10%; this result could be attributed to the age at which the diagnosis was made and to the difference in both employed diagnostic tools and definitions. The high percentage of children with DD could be also explained by school grade: Some numerical and calculation skill-gaining delays could still be present (at the end) of third grade; moreover, our experimental group consists exclusively of 8-year-old children, whereas most epidemiological studies include much wider age ranges. As shown by other authors (Devine et al., 2013; Dirks et al., 2008; Gross-Tsur et al., 1996; Lewis et al., 1994; Morsanyi et al., 2018), there is no significant gender predominance, although from a qualitative standpoint, data have showed a slight male predominance in children with LD; by analyzing the small sample of children with LD, the ratio was inverted, and we detected a female predominance, as previously observed in other studies (Desoete et al., 2004; Landerl & Moll, 2010, 2014).

Our results indicated that children with DD frequently have parents who have a low education level; this result is in accordance with some evidence reported in the literature concerning the role of SES factors like income and parent schooling levels and their link to calculation issues (Demir et al., 2015; Garon-Carrier et al., 2018). Furthermore, this result could also be explained by differences in the structural and functional organization of the brain as a function of the environment in which the children develop: It seems that social and economic conditions in which children grow up influence the growth and development of neural mechanisms (D’Angiulli et al., 2012; Schibli & D’Angiulli, 2013).

Comorbidity

Despite the high fluctuation rate caused by the use of a wide array of tests and evaluation criteria, our data also supported other research findings concerning DD prevalence in comorbidity compared to the prevalence of dyscalculia without other LDs. Some studies showed that 62% of children with DD also presented with reading and/or writing impairments, while only 4% of the overall study group population showed an isolated DD without any other comorbid LDs (Dirks et al., 2008; Lewis et al., 1994; Ramaa & Gowramma, 2002). The aforementioned data support some evidence that determined isolated DD to be between 1% and 3% (Lewis et al., 1994; von Aster & Shalev, 2007). Comorbidity between DD paired with dyslexia, DD with writing disorders, or DD accompanied by dyslexia and writing problems has been associated with low performances involving cognitive factors. Children with a mixed learning disorder (DD and dyslexia with/or without writing disorders) had lower scores on all school-mandated skills as well as attentional and visuospatial abilities; furthermore, the presence of DD associated with other LDs has been shown to have indicate greatly lower abilities in reading/writing issues when compared to dyslexia and/or writing disorder-only study groups.

Cognitive Factors and Neuropsychological Profile

Children with DD obtained lower scores compared to the TD children in all skills taken into consideration: fluid intelligence, learning skills (i.e., reading, writing, and reading comprehension), and cognitive functions, such as attention, working memory, and visuospatial abilities, which indicates significant impairment compared to the control group. It has been suggested that children who display math difficulties with/without reading difficulties show differences in their cognitive profiles (Moll et al., 2014; Szűcs, 2016). Several longitudinal studies have revealed that the working memory function is a strong predictor of mathematical performance (Allen et al., 2020; Geary, 2011; Mazzocco & Kover, 2007); several studies have revealed working memory impairments in individuals with DD (Mammarella et al., 2013; Passolunghi & Siegel, 2001).

Among the various factors that appear to indicate the presence of DD, our study found that the lack of attentional abilities indicated the DD children were significantly more compromised in relation to the TD children or children affected by other learning disorders (dyslexia and/or writing disorders, without dyscalculia). This result can be linked with the results from studies revealing significant attentional issues in children with DD compared to their typically developing peers (Andersson & Lyxell, 2007; D’Amico & Guarnera, 2005; Kuhn et al., 2016; McLean & Hitch, 1999; Shalev et al., 1995; Szűcs et al., 2013). The strong negative correlation with attentiveness is even more interesting. We suggest that this dysfunctional cognitive process can be associated with calculation and numerical processing difficulties. Furthermore, data analysis indicated a distinct lack of visuospatial skills in our DD group and a considerable positive association between such abilities and the scores obtained in math tests (i.e., those including addition beyond single digits). The results described above could support a hypothesis that suggests a strong link between spatial and numerical mental imaging (see Hubbard et al., 2005, for a review).

Regarding mathematical skills, the DD-only group obtained a BDE profile that may overlap with that of the DD in comorbidity with other LDs group, except for two subscales: semantic coding and number repetition. For semantic coding, it can be assumed that the best performances belonging to participants with DD associated with reading or writing disorders may be due to a difference in the type of dyscalculia. Indeed, we suggest that some typologies are more influenced by language-related aspects, even if these data are not evident from the analyses (which might be due to the exiguous numbers). Instead, the worst performances in number repetition could be attributed to transcoding difficulties common to writing and reading disorders, which could have a lesser impact on children with dyscalculia only.

Furthermore, the origin of dyscalculia seems to be related to attentional problems and visuospatial difficulties that also hamper reading and writing abilities, even to a lesser extent when compared to the linguistic deficiencies occurring in children with reading and/or writing disorders. When writing and/or reading impairment was paired with DD, a profile emerged that showed further learning difficulties, even compared to the LD-only group. It is therefore possible to hypothesize a continuum in reading–writing performances, mediated by visuospatial and attentional issues, which in the case of children with DD are associated with language-related impairments.

Implications for Practice

The current findings could have important implications for practitioners. First, our study showed a higher prevalence (approximately 10%) of DD and also indicated high comorbidity between dyscalculia and reading and/or writing disorders. These results are relevant for educators because they demonstrate that dyscalculia has a generalized impact on academic performances. Thus, educators should have an awareness of the complexity of children with dyscalculia, and it should be taken into account as a part of teachers’ training for administering screening tests and performing appropriate educational interventions. Second, a neuropsychological assessment should provide profiles of weaknesses and strengths. This information could support teachers in planning appropriate and alternative teaching activities and could help clinicians plan more accurate rehabilitation work and early interventions. The latter has been shown to improve self-perception, confidence, and academic success; consequently, early intervention should have a remarkably positive impact on school, social, and emotional well-being (Benassi et al., 2022; Scorza et al., 2018; Willcutt et al., 2013).

Limitations

It is important to note this study’s inherent limitations. First, children were recruited exclusively from a single city in northern Italy, and just a few participants with LD (n = 74) were identified. Second, attentional and visuospatial abilities were evaluated only through the use of questionnaires. Last, we cannot exclude the possibility that some false positives (acquisition delays) were present in the third-grade group from which we drew our participants. A re-evaluation of the same cohort is scheduled to be done at the end of the fifth primary school year.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Chiara Luoni

Barbara Fagiolini

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