Identifying the Neural Bases of Math Competence Based on

Identifying the Neural Bases of Math Competence Based on
Structural and Functional Properties of the Human Brain

Xueying Ren and Melissa E. Libertus

Abstrakt

■ Human populations show large individual differences in
math performance and math learning abilities. Early math skill
acquisition is critical for providing the foundation for higher
quantitative skill acquisition and succeeding in modern society.
Jedoch, the neural bases underlying individual differences in
math competence remain unclear. Modern neuroimaging tech-
niques allow us to not only identify distinct local cortical regions
but also investigate large-scale neural networks underlying math
competence both structurally and functionally. To gain insights
into the neural bases of math competence, this review provides
an overview of the structural and functional neural markers for
math competence in both typical and atypical populations of
children and adults. Although including discussion of arithmetic
skills in children, this review primarily focuses on the neural
markers associated with complex math skills. Basic number
comprehension and number comparison skills are outside the
scope of this review. By synthesizing current research findings,

we conclude that neural markers related to math competence
are not confined to one particular region; eher, they are char-
acterized by a distributed and interconnected network of
regions across the brain, primarily focused on frontal and pari-
etal cortices. Given that human brain is a complex network
organized to minimize the cost of information processing, ein
efficient brain is capable of integrating information from differ-
ent regions and coordinating the activity of various brain
regions in a manner that maximizes the overall efficiency of
the network to achieve the goal. We end by proposing that
frontoparietal network efficiency is critical for math compe-
tence, which enables the recruitment of task-relevant neural
resources and the engagement of distributed neural circuits
in a goal-oriented manner. Daher, it will be important for future
studies to not only examine brain activation patterns of discrete
regions but also examine distributed network patterns across
das Gehirn, both structurally and functionally. ■

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EINFÜHRUNG

Math competence is essential for success in fields that
require strong quantitative skills. It involves not only the
ability to perform mathematical procedures but also the
capacity to comprehend mathematical concepts and apply
them in different contexts to solve novel problems. Der
acquisition of math skills at an early age is important to
lay the foundation for the development of high-level quan-
titative skills and success in modern society ( Jordanien,
Kaplan, Ramineni, & Locuniak, 2009). Despite the impor-
tance of math competence, individuals show large differ-
ences in math performance and math learning abilities
(Artemenko et al., 2019), and the neural bases underlying
individual differences in math competence remain
unclear. Wichtig, early intervention is crucial for indi-
viduals with math learning deficits. Studies have shown
that remediation is most effective when implemented at
an early stage of development (Powell, Fuchs, Fuchs,
Cirino, & Fletcher, 2009; Räsänen, Salminen, Wilson,
Aunio, & Dehaene, 2009). daher, identifying the neu-
ral markers associated with math competence is of great
importance. This can not only help us to better under-
stand the origins of math learning deficits but also inform

University of Pittsburgh

the development of more targeted and effective inter-
Erfindungen. Zusätzlich, neural markers provide objective
measures of math competence that can complement
behavioral measures, allowing for more accurate tracking
of progress during learning and remediation. Daher,
understanding the neural mechanisms of math compe-
tence is crucial for improving the effectiveness of reme-
diation efforts.

It is well known that the human brain is a complex sys-
tem that comprises not only individual brain regions but
also distributed neural networks. The human brain is effi-
ciently organized by integrating information across various
brain regions to minimize the cost of information process-
ing while maximizing the overall efficiency of the brain
Netzwerke. Modern neuroimaging techniques allow us to
identify distinct local cortical regions and investigate large-
scale neural networks underlying math competence both
structurally and functionally. To gain insights into the
neural bases of math competence, this review aims to
find answers based on structural and functional proper-
ties of the human brain in both typical and atypical pop-
ulations of children and adults. Speziell, for atypical
Populationen, we will focus on individuals with math learn-
ing deficits. Math learning deficits are neurodevelop-
mental disorders that impair an individual’s ability to

© 2023 Massachusetts Institute of Technology. Published under a
Creative Commons Attribution 4.0 International (CC BY 4.0) Lizenz.

Zeitschrift für kognitive Neurowissenschaften 35:8, S. 1212–1228
https://doi.org/10.1162/jocn_a_02008

learn and perform math-related tasks. Dyscalculia is a
specific type of math learning deficit that affects the
development of arithmetical skills and other basic numer-
ical skills (Kuhl, Sobotta, Legascreen Consortium, &
Skeide, 2021; Kucian et al., 2014; Rykhlevskaia, Uddin,
Kondos, & Menon, 2009). When reviewing findings from
atypical population, we will focus on individuals with
math learning deficits including those with dyscalculia.

As math competence encompasses many different
skills, for studies involving adults, this review will selec-
tively examine the neural bases of relatively complex math
skills, such as evaluation of mathematical statements (z.B.,
“Any equilateral triangle can be divided into two right tri-
angles”; Amalric & Dehaene, 2016, 2019). For studies
involving children, we will also include fundamental math
abilities such as arithmetic skills that are commensurate
with the math skills young children master. Jedoch,
basic number comprehension and number comparison
skills are outside the scope of this review. Darüber hinaus, Wir
will consider whether neural markers associated with
math competence are unique to math or may be reflective
of academic achievement and cognitive abilities more
generally.

STRUCTURAL NEURAL MARKERS FOR
MATH COMPETENCE

Gray Matter Volume and Math Competence

Brain structural imaging studies have suggested an associ-
ation between gray matter properties and individuals’
math competence. Zum Beispiel, the “gray matter volume
hypothesis” posits that greater gray matter volume (GMV)
is associated with higher math competence. In support,
Li, Hu, Wang, Weng, and Chen (2013) found that GMV
in the sulcus (IPS) positively correlated with arithmetic
abilities in children. Jedoch, this study was cross-sectional,
leaving open the question whether similar relations between
GMV and math abilities exist across development. To
address this question, Price, Wilkey, Yeo, and Cutting
(2016) revealed that GMV in left IPS at the end of first grade
correlated with children’s math competence a year later at
the end of second grade. Ähnlich, GMV of multiple brain
regions such as IPS and prefrontal areas predicts long-term
gains in children’s math abilities (Evans et al., 2015).
Instead of using standardized math assessments (z.B.,
Woodcock-Johnson Tests of Achievement) as in previous
Studien, Wilkey, Cutting, and Price (2018) implemented
in-school math tests and also found that greater GMV in
bilateral hippocampus and right inferior frontal gyrus was
associated with higher math achievements in children
from third to eighth grade. Correlations between GMV
and children’s math abilities have also been revealed in
other brain regions such as angular gyrus, pFC, Und
occipito-temporal areas (Evans et al., 2015; Lubin et al.,
2013). Jedoch, those findings could not differentiate
the influence of brain maturation versus math learning

Erfahrung, which are strongly intertwined at early devel-
opmental stages, in explaining observed brain–behavior
correlations. Zusätzlich, most studies only measured cor-
tical anatomy at one time point and rarely examined the
GMV changes in the same sample at different developmen-
tal stages. Daher, further studies are needed to investigate
the relations between GMV changes and growth in math
abilities across development.

A similar relation between GMV and academic skills
exists in other domains such as reading (Torre & Eden,
2019; Johns et al., 2018). Interventional studies in reading
showed that effective reading training can induce GMV
erhöht sich (Krafnick, Flowers, Napoliello, & Eden, 2011).
Jedoch, there is a lack of intervention studies examining
the effects of math-related training on GMV, and the rela-
tion between training-induced changes in GMV and math
learning improvements. More research in this area is
needed to fully understand the connections between
GMV and math learning outcomes. Darüber hinaus, although
there is a consistent pattern between GMV and math com-
petence in children, few studies have examined the rela-
tions between GMV and math competence in adults.

Reduced GMV in brain regions such as parietal and fron-
tal cortex is an important neural marker associated with
math learning deficits. Zum Beispiel, children with calcula-
tion deficits had reduced GMV in the left IPS compared
with typically developing children (Isaacs, 2001). Subse-
quent studies in children with developmental dyscalculia,
a severe math learning disability, also found reduced GMV
in parietal cortex (including IPS; Cappelletti & Price, 2014;
Ranpura et al., 2013; Rykhlevskaia et al., 2009; Rotzer et al.,
2008), and frontal areas such as inferior frontal gyrus and
middle frontal gyri (Rotzer et al., 2008), within the fronto-
parietal system. Zusätzlich, GMV reduction has also been
observed in parahippocampal gyrus (Rykhlevskaia et al.,
2009), occipital-temporal cortex, and insula (Han et al.,
2013). Studies in adults with dyscalculia showed reduced
GMV in right parietal cortex relative to normal controls
(Cappelletti & Price, 2014). Jedoch, their findings do
not merit drawing causal inferences between abnormal
structural properties and math learning deficits because
it remains unclear whether the GMV reduction caused
math learning deficits or vice versa. Another important
issue is that individuals at the lower end of math compe-
tence often show higher rates of comorbidity with other
cognitive disorders such as dyslexia (Rubinsten & Henik,
2009), a reading learning disorder. Earlier studies have
shown that GMV reduction could also evidence dyslexia
(Eckert, Berninger, Vaden, Gebregziabher, & Tsu, 2016).
Daher, future studies should carefully differentiate struc-
tural neural markers for different learning deficits.

To sum up, differences in GMV could reflect individual
differences in math competence and other cognitive abil-
ities. Speziell, greater GMV in math-related brain
Regionen (z.B., IPS) is associated with higher math com-
petence. Im Gegensatz, reduced GMV may be associated
with learning difficulties in general, and reduced GMV,

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especially in the frontal and parietal regions, may be a
neural marker for math learning deficits in particular.

Cortical Thickness and Math Competence

Cortical gray matter is determined by cortical thickness
and cortical surface area, and studies suggest that they
reflect different neurodevelopmental mechanisms. Specif-
isch, cortical thickness is thought to reflect a neurodeve-
lopmental process of experience-dependent synaptic
pruning (Shaw et al., 2006) or myelination (Natu et al.,
2019) in response to skill acquisition and skill refining,
whereas cortical surface area is thought to reflect geneti-
cally determined cortical folding (Panizzon et al., 2009;
Kapellou et al., 2006). Although surface area has been
speculated to be influenced by experience-dependent syn-
aptic pruning like cortical thickness as well (Lyall et al.,
2015; Schnack et al., 2015), there is a lack of studies that
have examined the relation between cortical surface area
and math abilities specifically, making it difficult to deter-
mine the role of surface area in math competence. Daher,
the following discussion will mainly focus on the relation
between cortical thickness and math competence.

According to the “cortical maturation hypothesis,” cor-
tical thinning and increased surface area—both indicators
of cortical maturation—are associated with greater math
competence. Zum Beispiel, Schel and Klingberg (2017)
found that cortical thinning of right anterior IPS is associ-
ated with better math abilities in children older than 12
Jahre, and this region becomes more specialized for math-
ematics during development. Darüber hinaus, they also
revealed that thinner cortex in IPS was related to better
working memory and reasoning ability. In a longitudinal
study following children from kindergarten (5–6 years
alt) to second grade (7–8 years old), Kuhl et al. (2020)
revealed that cortical thinning is associated with better
math abilities in the right temporal lobe and left middle
occipital gyrus. zuletzt, math-gifted adolescents have thin-
ner cortex and larger surface area in the frontoparietal net-
arbeiten, which suggested maturation of the neural network
for math competence in adolescence (Navas-Sánchez
et al., 2016). Jedoch, it is important to note that although
there are correlations between structural properties and
math abilities, the exact role of localized cortical changes
for cognitive functioning remains unclear. It is also
unknown whether those cortical changes reflect math-
specific abilities or support cognitive functioning more
generally.

Studies with adults did not reveal correlations between
cortical thickness and math competence (Heidekum,
Vogel, & Grabner, 2020; Torre, Matejko, & Eden, 2020).
Zum Beispiel, in an MRI study with 89 typically developed
Erwachsene, Heidekum et al. (2020) investigated the associa-
tions between brain structures and math competence.
Jedoch, they did not find associations between cortical
thickness and math competence in math-related brain
regions such as IPS. One possible explanation for the

contradictory findings between children and adults could
be the differences in sample size, or the types and ranges
of math skills measured. Another possible explanation is
that as cortical thickness changes as a result of learning
and practice, adults do not engage in math as frequently
as children who are still undergoing formal math instruc-
tion, which may lead to less math-related cortical thickness
changes in adults (Torre et al., 2020). Zusätzlich, menschlich
cognition tends to reach a stable state in early adulthood,
and the effects of training or learning on cognitive perfor-
mance are typically limited (Neubauer & Fink, 2009). As a
Ergebnis, training-induced cortical changes, although still
möglich, may require a large amount of practice and train-
ing to achieve. From this perspective, it is possible that
cortical thickness may not be directly linked to math com-
petence, but rather to the amount of math practice and
training that an individual has had. This could lead to con-
fusion between the two factors. Future research should
aim to differentiate the effects of math training and actual
math competence on anatomical features.

This discrepancy between children and adults also
points to the critical role of cortical plasticity during formal
math acquisition. Tatsächlich, the importance of cortical plastic-
ity in math is corroborated by intervention studies, welche
showed that aerobic fitness training could improve chil-
dren’s math learning outcomes via cortical thinning
(Chaddock-Heyman et al., 2015), which furthers our
understanding of cortical plasticity as a function of training
and its importance in math achievement at early develop-
mental stages. Jedoch, its role in later development
needs more investigation.

Zusammenfassend, a combination of variation in brain struc-
tures such as GMV and cortical thickness could evidence
individual differences in math competence. Speziell,
greater GMV and cortical thinning merit greater neural
resources capacity and neural network efficiency, welche
further support higher math competence and other supe-
rior cognitive abilities. Wichtig, the structural neural
changes related to math competence are not confined
to one particular brain region but cover distributed brain
Regionen, and structural integrity across these regions is
critical for high-level math competence and efficient cog-
nitive functions.

White Matter and Math Competence

Individuals who exhibit superior math competence not
only rely on the efficient processing in gray matter but also
depend on the efficient communication between inter-
connected white matter structures ( Johansen-Berg,
2010). The integrity of the white matter plays a crucial role
in fostering and maintaining high-level math competence.
Diffusion tensor imaging is a powerful tool to assess struc-
tural connectivity within the human brain and investigate
the correlation between brain microstructure and cogni-
tive processes ( Johansen-Berg, 2010; Ben-Shachar,
Dougherty, & Wandell, 2007; Olesen, Groß, Westerberg,

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Zeitschrift für kognitive Neurowissenschaften

Volumen 35, Nummer 8

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& Klingberg, 2003). This non-invasive magnetic resonance
imaging technique uses the measurement of water diffu-
sion direction and orientation to gain insights into the
structural integrity of the white matter fiber tracts in the
Gehirn ( Jones, 2008). To date, most of the studies exploring
the relations between brain microstructure and math com-
petence have focused on children. These studies have
identified correlations between children’s math abilities
and fractional anisotropy, a metric used to quantify the
directional orientation of water diffusion in the brain, In
various regions including the corpus callosum connecting
the left and right hemispheres (Li, Wang, Hu, Liang, &
Chen, 2013; Cantlon et al., 2011; Hu et al., 2011), corona
radiata connecting the brain stem and the cerebral cortex
(Hu et al., 2011; van Eimeren, Niogi, McCandliss,
Holloway, & Ansari, 2008), inferior longitudinal fasciculus
connecting occipital and temporal cortices (Hu et al.,
2011; van Eimeren et al., 2008), and superior longitudinal
fasciculus connecting parietal, Hinterhaupt, and temporal
cortices with the frontal cortex (Hu et al., 2011; Tsang,
Dougherty, Deutsch, Wandell, & Ben-Shachar, 2009).
Speziell, in a study conducted with math-gifted
adolescents, Navas-Sánchez et al. (2014) found height-
ened white matter integrity in the frontoparietal, fronto-
striatal, and temporo-parietal tracts compared with
age-matched controls. This finding supports the hypothe-
sis that enhanced anatomical connectivity in these regions
underlies high-level math competence. Interessant, A
training study has shown that children’s behavioral
improvement in arithmetic problem solving after 2
months of math tutoring was positively correlated with
changes in the fronto-temporal white matter tract
(Jolles, Wassermann, et al., 2016). This finding highlights
a crucial relation between improvements in math abilities
and changes in white matter integrity in children. More
importantly, their results also provide novel insights into
the plasticity of white matter in childhood as a result of
learning, suggesting that the integrity of white matter
exhibits dynamic variations in response to learning (Jolles,
Wassermann, et al., 2016).

A few studies have also reported similar findings in
Erwachsene (Matejko, Price, Mazzocco, & Ansari, 2013; Transporter
Eimeren et al., 2010). Zum Beispiel, Matejko et al. (2013)
used diffusion tensor imaging to investigate the relation
between individual differences in white matter and perfor-
mance on the math subtest of the Preliminary Scholastic
Aptitude Test in adults. They found a positive correlation
between fractional anisotropy in the left parietal white
matter and math scores, suggesting that the microstruc-
tural integrity of white matter in the left parietal cortex is
linked to high levels of math competence. This study is
also the first to provide evidence for the association
between individual differences in white matter and math
competence as measured by a nationally administered
scholastic aptitude test, which provides important insights
into the neural mechanisms underlying math competence
bei Erwachsenen.

Reduced fractional anisotropy could evidence math
learning deficits. Zum Beispiel, Rykhlevskaia et al. (2009)
found decreased fractional anisotropy in the right temporo-
parietal white matter of children with dyscalculia com-
pared with typically developing children. Zusätzlich,
Kucian et al. (2014) suggested that dyscalculia may be
associated with poor white matter connections between
regions critical for mathematical processing, such as the
parietal, zeitlich, and frontal regions. Jedoch, eins
caveat is that correlations between brain microstructure
and math skills may be specific to a particular disorder
or an indirect consequence of neuropathology (sehen
Matejko et al., 2013, for a detailed discussion).

To summarize, these studies indicate a correlation
between individual differences in white matter structures
and math competence. The integrity of white matter plays
a crucial role in supporting high levels of math compe-
tence by enabling efficient communication across differ-
ent brain regions. Gleichzeitig, abnormalities in
white-matter integrity may evidence math learning defi-
cits. Further investigation is necessary to understand the
longitudinal changes in white-matter microstructure in
relation to the development of math abilities.

FUNCTIONAL NEURAL MARKERS FOR
MATH COMPETENCE

Neural Efficiency and Math Competence

Using advanced neuroimaging techniques such as fMRI,
earlier studies have suggested an association between
brain functional properties and math competence. Für
Beispiel, numerous studies have found relations between
individual differences in math competence and brain activ-
ities across different brain regions. The human brain is
organized in an efficient way by integrating and coordinat-
ing information and activities across various brain regions
to minimize the information processing cost and maxi-
mize the overall processing efficiency. According to the
neural efficiency hypothesis, individuals with superior
math performance tend to engage less neural resources,
resulting in less brain activity required for task completion,
compared with those with low levels of math competence
(Haier, 2016; Neubauer & Fink, 2009; Haier, Jung, Yeo,
Kopf, & Alkire, 2004). Support for this hypothesis comes
from studies with adults and children. Zum Beispiel, Jeon
and Friederici (2017) found that adults with high levels of
expertise in math showed less and more confined brain
activation in a small number of brain regions in a mathe-
matical hierarchy processing task, whereas adults with low
levels of expertise showed greater activation in broadly dis-
tributed brain regions. Jedoch, because this study lacked
variations in task complexity, it is difficult to judge whether
these findings would extend to other relatively more com-
plex tasks and how the brain activity patterns change as a
function of task complexity. Similar brain activation pat-
terns emerged in adolescents as well such that adolescents

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with better math competence showed decreased brain
activity during arithmetic problems solving in right IPS,
likely because of more precise representations of magni-
tudes, compared with adolescents with lower math com-
petence (Price, Mazzocco, & Ansari, 2013). Greater neural
efficiency has also been found to be associated with better
performance in other cognitive domains such as working
memory and executive control (see Neubauer & Fink,
2009, für eine Rezension).

Jedoch, there are also studies that suggest the oppo-
site, nämlich, that individuals with higher math abilities
show greater brain activation, instead of reduced activa-
tion, in various brain regions compared with those with
lower math competence. Zum Beispiel, Amalric and
Dehaene (2016) found that mathematicians showed
greater brain activation in the left visual number form area
relative to nonmathematicians in response to number
Reize. Wichtiger, by varying the problem diffi-
culty levels and strategy selection, they suggested a core
network consisting of IPS and bilateral inferior temporal
regions to be used exclusively for mathematical knowl-
edge representation (Amalric & Dehaene, 2019). Wie-
immer, the role of this core network in explaining other
higher-order math abilities remains unclear. Ein anderer
Studie, which utilized functional near-infrared spectros-
copy in conjunction with EEG, investigated the interaction
between math ability and arithmetic complexity. Der
study revealed that individuals with high levels of math
skills exhibited greater brain activation in regions such as
the superior temporal gyrus and inferior frontal gyrus,
compared with those with low levels of math skills, Wann
solving complex math problems (Artemenko et al., 2019).
Ähnlich, Grabner, Reishofer, Koschutnig, and Ebner
(2011) and Grabner et al. (2007) found that individuals
with high levels of math competence exhibit greater
activation in the left angular gyrus when solving math
problems, compared with those with low levels of math
competence. Their finding highlights the significance of
language-mediated processes in math problem solving
and the crucial role of the angular gyrus in math cognition
among those with high levels of math competence. Daher,
individuals with superior math competence, verglichen
with those with lower math competence, tend to show
greater cortical activation and higher usage of task-related
neural resources in brain regions such as frontal, parietal,
and temporal cortices possibly to strengthen their cogni-
tive processing (see Dehaene, Piazza, Pinel, & Cohen,
2003, for a discussion of parietal region in math cognition;
O’Boyle et al., 2005).

One possible explanation for the inconsistency in the
findings regarding the neural efficiency hypothesis is that
brain activation is modulated by various task demands
(Chochon, Cohen, van de Moortele, & Dehaene, 1999).
Mit anderen Worten, tasks that are relatively complex or rely less
on practice and experience may require greater engage-
ment of task-related brain regions or sophisticated strate-
gies to complete the tasks successfully ( Waisman,

Brunner, Grabner, Leikin, & Leikin, 2023). This argument
is consistent with the finding that when the complexity of
the task increases and familiarity with the task decreases,
adolescents with higher math abilities showed greater acti-
vation in distributed brain regions (z.B., frontal, parietal
Regionen, anterior cingulate gyrus; Desco et al., 2011) com-
pared with average-ability controls. This indicated that
individuals with higher math competence will engage
more task-relevant neural resources when necessary.
Jedoch, during relatively simple, familiar math tasks,
people with higher math competence may engage a more
automatic process that attenuates the dependency on
other cognitive resources (z.B., executive control and
attention), resulting in less effortful cognitive processing
and less brain activation to solve the corresponding tasks.
Im Gegensatz, individuals with lower math competence may
rely more on cognitive resources (z.B., attention, inhibi-
tory control, or working memory) to solve the problems,
which leads to stronger activation in brain regions such as
frontal areas (Klimesch, Sauseng, & Hanslmayr, 2007).

Several studies support the notion that task difficulty
and familiarity modulate the relation between brain activa-
tion and math competence. Zum Beispiel, Zhang, Gan, Und
Wang (2015B) implemented numerical inductive reason-
ing tasks with high or low complexity and demonstrated
how neural efficiency was modulated by task complexity.
Speziell, they found that math-gifted adolescents
showed stronger brain activity for the complex task and
less brain activity for the easy task in the frontoparietal net-
arbeiten, compared with average achieving adolescents. In
addition, Amalric and Dehaene (2018) found stronger
brain activation in pFC during complex, unfamiliar
mathematical statement judgments and reduced frontal
activation during simpler, more routinized facts, welche
suggested the important role of frontal areas in information
manipulation when needed.

To sum up, task complexity and familiarity modulate
brain activation and neural efficiency related to math
ability. Speziell, individuals with high levels of math
competence tend to exhibit focused and localized brain
activity during relatively simple and familiar tasks, wohingegen
those with low levels of math competence tend to exhibit
diffuse and widespread brain activation. Umgekehrt, Dauer-
ing relatively complex, less familiar math tasks, Menschen
with high levels of math competence tend to activate
additional neural resources presumably to achieve effi-
cient neural processing.

Training-induced Brain Activity Changes and
Math Competence

Brain activity can also be modulated by the effect of
short-term training (Neubauer, Grabner, Freudenthaler,
Beckmann, & Guthke, 2004). Studies on complex arith-
metic training, specifically complex multiplication
Ausbildung, in adult participants have revealed a decreased
activation in the frontal gyri, IPS, and superior parietal

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lobule, while displaying an increased activation in the
angular gyrus (sehen, z.B., Zamarian, Ischebeck, & Delazer,
2009, für eine Rezension). This shift in brain activity indicates
a transition from relying on effortful and procedural
processes to utilizing memory and retrieval-based
strategies (Grabner & De Smedt, 2012; Grabner et al.,
2009; Ischebeck, Zamarian, Schocke, & Delazer, 2009;
Ischebeck, Zamarian, Egger, Schocke, & Delazer, 2007;
Ischebeck et al., 2006; Delazer et al., 2003, 2005). Consis-
tently, developmental imaging studies in the field of
arithmetic reasoning also support the notion that the
automation of mathematical processes is accompanied
by a shift in activation from frontal to parietal regions
(Rivera, Reiss, Eckert, & Menon, 2005). Wichtig,
Wirebring et al. (2015, 2022) conducted a study comparing
the effectiveness of two different math training methods—
one involving the repeated practice with solutions provided,
and the other focusing on fostering creative problem-
solving skills. Individuals who underwent training in
creative thinking exhibited improved performance in math
problem solving, as well as brain activation changes in
several regions such as the angular gyrus, which facilitates
the engagement of relevant brain regions for the task and
optimizes the efficiency of neural resource allocation.

Studies exploring the impact of short-term training on
Kinder, although scarce, have demonstrated that,
through proper training, children can experience
improved neural processing and function as well. Für
Beispiel, Soltanlou et al. (2018) showed that arithmetic
training leads to not only improved math performance in
Kinder, but also a reduction in brain activation in the
frontoparietal network, as assessed through a combina-
tion of functional near-infrared spectroscopy and EEG.
Außerdem, prior research has revealed that a decrease
in frontoparietal activation is accompanied by increased
activation in the hippocampus, and greater connectivity
between the hippocampus and frontal regions is corre-
lated with higher math competence in children (Qin
et al., 2014; Supekar et al., 2013). It is possible that individ-
uals with superior math abilities have stronger memory
and knowledge representation for mathematics, com-
pared with those with low levels of math competence.
The heightened activation observed in the hippocampus
of such individuals may indicate a more robust memory
representation as a result of semantization and/or consol-
idation, thereby contributing to their superior math
competence.

Zusammenfassend, all these studies suggest that training can
induce changes in brain activity and enhance neural effi-
ciency by selectively engaging relevant brain regions to
varying degrees, depending on the specific task demands.
Speziell, participants may initially engage in effortful
Verarbeitung, relying heavily on cognitive resources such
as working memory to process information. Jedoch, als
they become more skilled in the task, they may develop
stronger memory and math knowledge representations,
leading to a shift toward more automatic and efficient

processing modes that require less cognitive efforts for
task completion (Zhang et al., 2015B; Neubauer & Fink,
2009; Neubauer et al., 2004). It is worth noting that the
changes in brain activity may vary based on the type of
instruction received (Delazer et al., 2005). Somit, future
Studien, particularly in children, should investigate the
effects of different learning environments and instruc-
tional methods on brain activity changes to enhance chil-
dren’s math learning outcomes (Grabner & De Smedt,
2012).

Aberrant Neural Responses Mark Math
Learning Deficits

Aberrant neural responses may be a neural marker for
math learning deficits. Zum Beispiel, children with math
learning deficits showed either hyper-activation or hypo-
activation in multiple brain regions, including parietal,
occipital-temporal, and prefrontal regions during math
problem solving compared with peers with average math
abilities (Peters & De Smedt, 2018; Iuculano et al., 2015;
Berteletti, Prado, & Booth, 2014; De Smedt, Holloway, &
Ansari, 2011; Molko et al., 2003). Darüber hinaus, Kinder
with math learning deficits showed a lack of task
difficulty-related modulation of neural activity. Für
Beispiel, Ashkenazi, Rosenberg-Lee, Tenison, Und
Menon (2012) found that typically developing children
showed increased brain activity for complex arithmetic
problems, whereas children with dyscalculia did not, sug-
gesting that children with dyscalculia do not modulate
their brain activity in accordance with task complexity.
Jedoch, their findings do not allow for causal claims
between math learning deficits and brain activity patterns
as it is unclear whether dyscalculia caused the lack of task-
based brain activity modulation or vice versa. Zusätzlich,
one should interpret the results with caution because of
the comorbidity with other cognitive disorders (z.B., dys-
lexia, attention-deficit/hyperactivity disorder; Rubinsten &
Henik, 2009). Zum Beispiel, arithmetic skill, especially
arithmetic fact retrieval, is associated with reading ability
(Chu, vanMarle, & Geary, 2016; De Smedt & Boets,
2010), and such aberrant brain activity patterns have also
been found in children with dyslexia. In an fMRI study,
Evans, Flowers, Napoliello, Olulade, and Eden (2014)
investigated the differences in brain activity between chil-
dren with and without dyslexia during arithmetic addition
and subtraction operations. They found that children
without dyslexia showed strong activation in right supra-
marginal gyrus only for subtraction, whereas dyslexic chil-
dren engaged this region during both subtraction and
addition problems, suggesting a less optimal route for
retrieval-based arithmetic problems and a lack of task-
based brain activity modulation.

Zusammenfassend, aberrant brain activity might be because of
lower efficiency in engaging task-related brain areas,
which results in less optimal routes for problem solving.
Darüber hinaus, aberrant brain activation may extend beyond

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the domain that is affected by the particular learning def-
icit as evidenced by difference in brain activation during
math problem solving for children with and without dys-
lexia. Daher, one challenge for future studies is to address
the heterogeneity in children with math learning deficits,
which may cause inconsistent finding across different
Studien ( Jolles, Ashkenazi, et al., 2016). To investigate
the neural markers that are specific to math learning def-
icits, subject selection and matching criteria based on
other cognitive abilities should be carefully implemented.

Neural Representations and Math Competence

Despite the common activation of broad brain regions
associated with math cognition, it is possible that the neu-
ral representations within these regions may vary among
individuals. Mit anderen Worten, although the general regions
of the brain that are activated during mathematical tasks
may be similar across individuals, neural representations
within these regions may still differ. These differences in
neural representations could potentially serve as an alter-
native neural marker for math competence. Zum Beispiel,
using a multivariate representational similarity method,
Ashkenazi et al. (2012) found that children with develop-
mental dyscalculia showed less differentiated neural rep-
resentations for addition problems at different difficulty
levels, compared with typically developing children. Wie-
immer, it is unclear whether a lack of representational differ-
entiation also extends to other numerical operations in
children with dyscalculia. Chen et al. (2021) further inves-
tigated this question and revealed that children with low
math abilities showed less differentiated neural represen-
tation patterns between addition and subtraction prob-
lems in various brain regions including fusiform gyrus
and IPS than children with greater math abilities. Diese
studies highlighted the importance of examining the neu-
ral representations within these regions, rather than solely
relying on the activation of broad regions, to fully under-
stand the neural underpinnings of math competence. Als
addition and subtraction problems are only two basic math
skills, it would be useful to further implement multivariate
approaches to investigate the neural representations for
other relatively complex math problems. Zusammenfassend,
based on the evidence reviewed above, individuals with
math learning deficits not only exhibit aberrant neural
activity but also fail to modulate task-related neural
responses and generate distinct neural representations
for different math problems.

Jedoch, such aberrant neural responses and represen-
tations could be altered and normalized to be indistin-
guishable from those of typically developing peers with
effective training (Iuculano et al., 2015). Speziell, nach
an 8-week, one-on-one cognitive tutoring intervention,
aberrant functional brain responses in regions such as
frontal and parietal brain areas were normalized to be
comparable to typically developing children. Zusätzlich,
multivariate pattern analyses showed that the brain activity

patterns in children with math learning deficits were also
altered to be indistinguishable from those of typically
developing peers. Daher, their findings suggest that
tutoring-induced functional changes, manifested in nor-
malized brain activation and neural representations, could
be an effective intervention strategy in children with math
learning deficits. Wichtig, they used rigorous quantita-
tive approaches to demonstrate the tutoring-induced
functional brain plasticity. More experimental studies like
these are needed to demonstrate the causal link between
math competence and various neural markers associated
with math abilities.

Functional Connectivity and Math Competence

Another neural marker of math competence may be func-
funktionale Konnektivität, which is based on temporal coupling
of neural responses to examine context- and stimulus-
dependent interactions across different brain regions
( Jirsa & McIntosh, 2007). According to the parieto-frontal
integration theory (P-FIT), stronger connectivity and inte-
gration between frontal and parietal regions enable effi-
cient communication among regions, which underpins
the neural basis for higher intelligence ( Jung & Haier,
2007). The important role of the frontoparietal network
has been manifested in other cognitive abilities as well
such as in cognitive control, Arbeitsgedächtnis, adaptive
behavior, and creative problem solving (Barbey, Colom,
Paul, & Grafman, 2014; Barbey, Colom, & Grafman,
2013; Barbey et al., 2012; Cole, Yarkoni, Repovš, Anticevic,
& Mutiger, 2012). Zum Beispiel, Sheffield et al. (2015) found
positive correlations between functional integration of the
frontoparietal network and overall cognitive ability, welche
suggested that a greater functional integration of the fron-
toparietal network is crucial for supporting better overall
cognitive functioning.

As math competence tends to be linked to general intel-
ligence and other domain-general cognitive abilities such
as working memory and executive functioning (Menon,
2014, 2016; Desco et al., 2011; Mrazik & Dombrowski,
2010), P-FIT may also be applicable to math competence.
Speziell, evidence in support of P-FIT comes primarily
from studies with children. Mental rotation tasks such
as mental rotation of 3-D objects have been suggested as
well-suited for assessing math competence as it is a
complex visuospatial task that involves creation and
manipulation of mental images (Gill, O’Boyle, &
Hathaway, 1998), which are two important factors for
high-level mathematical thinking and reasoning (O’Boyle,
Benbow, & Alexander, 1995). Greater functional connec-
tivity between frontal and parietal regions is required for
successful completion of such complicated tasks. Für
Beispiel, in an fMRI study with a mental rotation task,
Prescott, Gavrilescu, Cunnington, O’Boyle, and Egan
(2010) found increased frontoparietal connectivity in
math-gifted adolescents compared with those with aver-
age math ability. Ähnlich, O’Boyle et al. (2005) revealed

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that mathematically gifted male adolescents showed
higher activation in the frontoparietal network and ante-
rior cingulate during mental rotation tasks. Similar positive
associations between frontoparietal connectivity and math
abilities have been revealed in other tasks as well. Für
Beispiel, Emerson and Cantlon (2012) found that greater
frontoparietal connectivity during a natural video watching
task was associated with greater math abilities in children.
The frontoparietal network is also activated during arith-
metic problem solving, and the activity of the network is
modulated by strategy use, expertise, and training (sehen
Peters & De Smedt, 2018, für eine Rezension). Such patterns also
exist in resting-state data where increased functional con-
nectivity between number-related areas (z.B., number
form area) and frontoparietal network is associated with
greater math abilities across development (Nemmi, Schel,
& Klingberg, 2018). Darüber hinaus, the frontoparietal network
has been suggested as a flexible hub to facilitate adaptive
and novel task performance (Cole et al., 2013).

Consistent findings regarding the frontoparietal net-
work do not rule out the important roles that other neural
connections may play for math competence. In other
Wörter, at the subject level, there might be individual differ-
ences in strategy selection or engagement of different neu-
ral circuits, and only the frontoparietal network might
emerge consistently across an entire group of participants.
Jedoch, other brain regions and their neural connec-
tions may also be involved in math competence. Für
Beispiel, previous studies have suggested that the poste-
rior inferior temporal cortex is an important region in
mathematical processing, and it exhibits high functional
connectivity with the IPS (Hermes et al., 2017; Daitch
et al., 2016). Zusätzlich, utilizing intracranial EEG during
a mental arithmetic task, Pinheiro-Chagas, Daitch, Parvizi,
and Dehaene (2018) demonstrated that the posterior
inferior temporal cortex plays a crucial role in the early
identification of problem difficulty and mathematical
Verarbeitung. In a meta-analysis, Arsalidou, Pawliw-Levac,
Sadeghi, and Pascual-Leone (2018) revealed that regions
such as the insula and claustrum may play critical roles
in children’s math problem solving. The hippocampus
has also been suggested as an important brain region for
math competence especially for math fact representation,
and its connectivity strength to the frontoparietal network
could be an indicator of math competence (Qin et al.,
2014; Supekar et al., 2013; Cho et al., 2012). Jedoch,
most studies have mainly focused on relatively strong
connections of highly correlated brain regions (z.B., Die
frontoparietal network) and have held the assumption that
“stronger connectivity is always better.” Nevertheless, es ist
crucial to acknowledge that both strong and weak connec-
tions play important and different roles in brain function-
ing (Schwartz et al., 2021; Cole et al., 2012; Gallos, Makse,
& Sigman, 2012). More critically, weak connections, welche
are often overlooked, may largely contribute to individual
differences in higher cognitive functioning (Santarnecchi,
Galli, Polizzotto, Rossi, & Rossi, 2014). Zum Beispiel, es ist

possible that the relatively weaker connections between
claustrum and the frontoparietal network could evidence
individual differences in math competence. Future studies
should explore how connections between different brain
regions and connectivity strengths relate to individual
differences in math competence.

Zusammenfassend, functional connectivity could evidence
individual differences in math competence, and enhanced
functional connectivity is a fundamental component for
higher math competence. Speziell, functional connec-
tivity within the frontoparietal network not only plays a
critical role in math competence but also supports other
cognitive processes more generally. Individuals with high
levels of math competence or overall cognitive abilities
may rely on the frontoparietal network for effective neural
communication and information processing. Wichtig,
future studies should aim to further explore the important
roles of relatively weak neural connections in explaining
individual differences in math competence.

Functional Dysconnectivity and Math
Learning Deficits

Functional dysconnectivity, such as abnormal functional
integration and aberrant connectivity, may be a neural
marker for math learning deficits. Zum Beispiel, hypercon-
nectivity between IPS and multiple brain regions (z.B., Die
frontal and parietal regions), along with weaker perfor-
mance in math problem solving, was observed in children
with math learning deficits (Rosenberg-Lee et al., 2015).
Jolles, Ashkenazi, et al. (2016) used task-free MRI to inves-
tigate the differences in intrinsic functional connectivity
of IPS between typically developing children and children
with math learning deficits. They found that children with
math learning difficulties showed hyperconnectivity of
IPS with multiple brain regions including those in the
bilateral frontoparietal network. Darüber hinaus, a machine
learning classification algorithm showed that aberrant
IPS connectivity patterns can discriminate children with
math learning deficits from typically developing children.
Daher, these findings suggested that children with math
learning difficulties, instead of showing dysfunction of IPS
allein, may show aberration in the connectivity patterns,
and aberrant IPS connectivity can be a neural marker for
math learning deficits (see Dehaene, Molko, Cohen, &
Wilson, 2004, for a discussion).

One possible explanation why math learning deficits are
linked to aberrant functional connectivity is that a greater
engagement of multiple brain circuits may cause intrusion
of irrelevant information, resulting in interference and less
optimal routes for problem solving (Rosenberg-Lee et al.,
2015). This is supported by behavioral evidence that chil-
dren with math learning deficits showed poor inhibition of
irrelevant information when attempting to retrieve arith-
metic facts from long-term memory (Geary, Hoard, &
Bailey, 2012). Another possible explanation would be that
hyperconnectivity may result in less flexibility and

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efficiency in engaging and modulating task-relevant brain
circuits (Jolles, Ashkenazi, et al., 2016; Uddin et al., 2015).
Daher, atypical functional brain circuit dynamics may con-
tribute to math learning deficits. As previous studies
mainly focused on the role of IPS connectivity in math
competence, future studies should aim to explore the
roles of aberrant functional connectivity of other brain
Regionen, besides IPS, in explaining math learning deficits
as well as other learning difficulties.

Frontoparietal Network Efficiency Theory

Math competence is not only related to the activation of
individual cortical regions (see Menon, 2014, für eine Rezension),
but also to the interconnection between different brain
Regionen, as indicated by the functional neural markers
reviewed above. Individuals with high levels of math com-
petence are expected to exhibit high neural efficiency as
well as high functional connectivity across brain regions
to accomplish the task successfully. The human brain is
known to be organized in complex neural networks that
support high efficiency, with the goal of minimizing the
cost of information processing while maximizing the
capacity for growth and adaptation (Bullmore & Spurns,
2012). In diesem Kontext, information is processed not only
by functionally segregated brain areas but also by func-
tional integration across spatially distributed brain regions
through their dynamic interactions for efficient informa-
tion processing (Spurns, Tononi, & Edelman, 2000;
Alexander, O’Boyle, & Benbow, 1996). The functions of
individual brain regions might be important during early
stages of developing cortical resources for mathematical
learning, and then their relations with other higher-order
cognitive areas are slowly forged to form an intercon-
nected and dynamic neural network to support high levels
of math competence (Alexander et al., 1996).

Daher, to better understand the neural basis of math
competence, we propose examining the neural process-
ing at the network level, speziell, by assessing fronto-
parietal network efficiency. The frontoparietal network is
a key network involved in math processing, orchestrating
the exchange of information between task-related brain
Regionen, both within the network itself and between the
frontoparietal network and other networks, in a way that
maximizes efficiency in the overall information process-
ing. Daher, to better understand the neural mechanisms
underlying math competence, it is necessary to not only
examine the internal efficiency within the frontal–parietal
Netzwerk, but also its interactions with other brain net-
funktioniert. This approach, which we term the frontoparietal
network efficiency theory (FP-NET), will provide deeper
insights into how different neural networks work together
to support math abilities and their relations to individual
differences in math competence, offering a more compre-
hensive understanding of the neural mechanisms underly-
ing math competence beyond what can be explained by
the function of individual brain regions alone.

The FP-NET represents a theoretical framework that
synthesizes various components of neural efficiency
theory and frontoparietal integration theory, with a partic-
ular emphasis on overall brain network efficiency. It cap-
tures how the brain processes information from a different
perspective based on the overall performance of the brain
Netzwerke, and how it relates to individual differences in
math competence. FP-NET embodies two key aspects:
(1) the efficiency in engaging the frontoparietal circuit;
(2) the efficiency that uses the frontoparietal network as
a functional hub to integrate information across networks
and enable optimal routes based on task demands.

FP-NET postulates that the frontoparietal network is
efficiently engaged and used as a functional hub to inte-
grate information across different brain networks,
enabling the formation of optimal routes for rapid infor-
mation transfer between discrete brain regions based on
task demands. Task demand is determined by the extent
and type of neural processing required to complete the
Aufgabe, which can depend on factors such as the cognitive
demands (z.B., attention, Arbeitsgedächtnis), individual
differences (z.B., expertise, prior knowledge), and/or the
interplay between them. Different types of tasks require
different types of neural processing and pose varying cog-
nitive demands between and within individuals, which will
engage the frontoparietal network to different degrees.
Daher, FP-NET requires the human brain to flexibly activate
and coordinate these brain regions and leverage the func-
tions of different brain networks in response to the current
task demands. This flexibility necessitates the dynamic
adjustment of the strength and pattern of connectivity
between these regions based on task complexity to maxi-
mize the overall information processing efficiency.

Put differently, brain networks can be seen as a complex
map that consists of streets connecting different locations.
For relatively simple, familiar tasks, higher efficiency can
be achieved by taking a direct route from one region
(z.B., frontal area) to another (z.B., parietal area) for faster
information transfer to meet the goal (Figure 1A for F → P
pathway). Im Gegensatz, for relatively complex, unfamiliar
tasks, multiple paths will be engaged, and information will
travel to the hub (P), where information will be integrated,
from different sources and locations (z.B., F and D in
Figure 1B). It is possible that the efficiency for the direct
route (F → P) is high, but the global efficiency of integrat-
ing the information from different paths (especially D →
P) might be low, which will likely increase the information
processing cost (Figure 1B). Daher, sacrificing some effi-
ciency of the direct route (F → P) and increasing efficiency
of other task-related routes (z.B., D → P) might increase
the overall global efficiency for information processing
(Abbildung 1C).

One important aspect of FP-NET is that it does not
attempt to supersede previous theories in the field; eher,
it proposes a complementary approach to investigate the
neural basis of math competence based on network effi-
ciency. FP-NET is consistent with earlier findings that

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Figur 1. Schematic of the frontoparietal network efficiency theory (FP-NET). (F) represents frontal areas, (P) represents parietal areas, (C) Und (D)
represent other brain regions relevant for solving a specific, math-related task. Arrows represent information transfer, and the width and patterning of
the arrows represents efficiency for information transfer. The wider and more solid the arrow, the higher the efficiency and faster information
transfer. (A) illustrates high efficiency in engaging a direct route connecting frontal (F) and parietal (P) areas for relatively simple, familiar tasks. (B)
Und (C) illustrate what might happen during more complex, unfamiliar tasks when information will travel to the hub (P) from different sources (z.B.,
F and D). Speziell, (B) illustrates the situation where the efficiency of the direct route (F→P) is high, but the global efficiency is low because
efficiency of the other task-relevant route (D→P) is low. (C) illustrates that to achieve higher global efficiency, efficiency of the direct route (F→P)
sometimes will need to be sacrificed to enhance the efficiency of another task-relevant route (D→P).

human intelligence is related to how efficiently the brain
integrates information from multiple brain regions (Transporter
den Heuvel, Stam, Kahn, & Pol, 2009), and that the effi-
ciency in re-organizing brain networks based on current
task demands is positively correlated with general intelli-
gence (Schultz & Cole, 2016). Speziell, Zhang, Gan,
and Wang (2015A) found that when comparing a resting
state to a deductive reasoning task, individuals with higher
math competence showed a significant frontoparietal
network shift from modular subsystems toward a globally
efficient network architecture. Mit anderen Worten, based on
different task demands, there is a significant change of
the frontoparietal network from being modular and char-
acterized by locally clustered subsystems toward a higher
globally efficient network with shorter global connectivity
paths and better interconnected brain regions. This shift in
functional network architecture promotes task-related
functional intercommunication across discrete brain
regions in a timely manner. FP-NET aligns well with
small-world network theory as well in that brain networks
consist not only of dense local connections to reduce
wiring cost but also long-distance connections with short
paths to support global information processing (Bassett &
Bullmore, 2006, 2017; Watt & Strogatz, 1998). FP-NET is
also in line with the “flexible hub” theory that the fronto-
parietal network could rapidly and flexibly update its
global connectivity to support adaptive behaviors (Cole,
Mutiger, & Meiran, 2017; Cole et al., 2013).

Gesamt, FP-NET posits increased speed of information
transfer and reduced information processing costs, Profi-
moting efficient communication within the frontoparietal
system and across different brain networks in a goal-
oriented manner. Daher, FP-NET reflects the capacity and
flexibility of brain systems in engaging different brain cir-
cuits dynamically and efficiently. Wichtig, FP-NET

provides a different perspective in understanding of the
neural mechanisms underlying math competence beyond
the function of individual brain regions alone, highlighting
the importance of network-level processing and the inter-
actions between brain regions in understanding higher
cognitive functions.

Although FP-NET is a promising framework, weiter
investigation is required to fully understand the neural
mechanisms underlying math competence, the extent to
which network efficiency is a crucial component and its
implications. To test FP-NET, future studies should inves-
tigate how the frontoparietal network dynamically inter-
acts with other brain regions during different tasks, Und
how it changes as a function of ability level, Erfahrung,
Alter, und so weiter. Speziell, systematic task manipula-
tions are required (z.B., task complexity or strategy use)
to investigate to what extent different brain networks are
recruited, and how they relate to individual differences in
math competence. To investigate the interaction and con-
nectivity patterns between specific regions, researchers
can use methods such as TMS or transcranial direct cur-
rent stimulation to temporarily disrupt or enhance the
Konnektivität, and then observe changes in task perfor-
Mance. This approach can help to elucidate the role of a
particular brain region in a cognitive process and provide
insights into the interactions between different regions.
To test the network efficiency, one common method is
through graph theoretical analysis of functional connectiv-
ity data. These data could be derived from neuroimaging
Techniken, such as fMRI or EEG, and allows for the char-
acterization of the topology of brain networks and the
quantification of their efficiency based on various metrics,
such as path length, Clusterkoeffizient, and between-
ness centrality. This approach can provide insights into
how the frontoparietal network interacts with other brain

Ren and Libertus

1221

regions to support mathematical thinking and how these
interactions relate to individual differences in math
competence. Jedoch, it is important to note that the
appropriate method for evaluating frontoparietal network
efficiency may depend on the specific research question
and the type of data available, which requires further
investigation.

Zusammenfassend, empirical testing of FP-NET would require
a combination of neuroimaging techniques and experi-
mental manipulations of neural activity and connectivity.
By comparing the neural response patterns of individuals
performing simple and complex tasks and manipulating
connectivity strength, along with novel analytic approaches,
researchers can gain a better understanding of how the
brain optimizes its processing efficiency, the factors that
influence this optimization, and how this process relates
to individual differences in math competence. Daher, FP-
NET offers a new perspective and a starting point for
future research in understanding the neural basis of math
competence.

CONCLUSIONS AND FUTURE DIRECTIONS

Understanding the neural bases of math competence
based on both structural and functional brain properties
is of utmost importance. Previous studies have primarily
focused on the important role of IPS in numerical process-
ing (z.B., Schel & Klingberg, 2017; Menon, 2014). Wie-
immer, neural markers related to math competence are
not confined to one particular region; eher, they are
characterized by a distributed and interconnected neural
network across the brain, primarily focused on frontal
and parietal cortices. Wichtiger, the structural
integrity and functional efficiency across these regions
are critical for higher math competence, which allows
for recruiting the most task-relevant neural resources
and neural circuits during problem solving. Daher, to gain
a comprehensive understanding of the neural underpin-
nings of math competence, it is essential for future studies
to not only investigate the local brain activity patterns and
functional connectivity (z.B., Emerson & Cantlon, 2012)
but also examine the neural processing efficiency at the
network level.

Jedoch, many questions remain to be answered. Für
Beispiel, it remains unclear if the structural and functional
neural markers are specifically related to math or more
domain-general properties that can be found across a
broad range of cognitive skills. Future studies should
aim to carefully control for or investigate the influence of
other cognitive factors (z.B., executive control, working
Erinnerung) on math competence at the neural level. Für
Beispiel, one could implement control tasks that are not
mathematical in nature but similar in their cognitive
demands otherwise, taxing attention, inhibition, and so
forth, to examine what differences in brain activities are
specific to math and which ones can be found even in con-
trol tasks. These studies will also help to explain how much

of the differences in math competence between typical
and atypical populations can be explained by domain-
general factors as compared with domain-specific factors.
Zusätzlich, previous studies have most often studied
structural and functional neural features separately; Wie-
immer, they are highly related. Brain structural properties
can be seen as tools, and their uses are the functions.
Although individuals may possess similar tools, they may
use the tools differently. Zum Beispiel, a flathead screw-
driver could be used to tighten a Phillips screw or replace
a chisel, although it will likely not work very well in either
Fall. Daher, it is important to know what tools are at an
individual’s disposal, and it is also critical to know how
individuals use the tools differently. Future studies should
examine these two features at the same time to investigate
the relations between them across development and their
unique roles in explaining individual differences in math
competence.

Most of the studies reviewed here do not permit conclu-
sions about the causal relations between structural and
functional properties of the brain and math abilities. Das
is particularly critical for studies on math learning deficits.
One possible solution to this problem has been imple-
mented in studies on dyslexia where they matched
children with dyslexia not only to age-matched typically
developing children, but also to ability-matched children
of younger age. In one such study, dyslexic children
showed reduced brain activation in parietal and occipito-
temporal regions relative to age-matched and ability-
matched children, suggesting that the hypo-activation
might be the cause of dyslexia (Hoeft et al., 2007). Similar
approaches with strict subject selection and matching
criteria could be applied in the math domain to further
our understanding of the neural markers of math learning
deficits.

Zusätzlich, how structural and functional neural prop-
erties change as a function of age, Erfahrung, or interven-
tion, and their relations with math competence, requires
further investigation. Longitudinal approaches could be
the first step toward this endeavor to track neural changes
and examine possible correlations between different
neural features and math competence or other cognitive
abilities across developmental stages (z.B., Price et al.,
2016). Wichtiger, interventional studies are
needed to shed light on how to facilitate math learning,
and whether different interventions affect neural markers
of math abilities in the same way. Although it is possible
that different interventions may yield similar outcomes
behaviorally, it stands to reason that interventions that
yield brain patterns more similar to those associated with
higher math competence may ultimately be superior to
interventions that are not associated by normalization in
neural markers.

Endlich, several methodological approaches merit
consideration for future studies. Most studies reviewed
here have used MRI techniques, which have limitations
in temporal precision. Using more temporally precise

1222

Zeitschrift für kognitive Neurowissenschaften

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techniques such as EEG or near-infrared spectroscopy
alone or in conjunction with MRI would allow for a more
complete picture of the underlying neural markers associ-
ated with math abilities. zuletzt, meta-analyses of neuroim-
aging data are needed as well to synthesize findings in the
current literature (sehen, z.B., Houdé, Rossi, Lubin, & Joliot,
2010, for an example of a neuroimaging meta-analysis on
reading and other cognitive functions). Further investiga-
tion toward aforenoted directions will undoubtedly help
gain more insights into understanding the neural bases
of math competence.

Danksagungen

We would like to thank Julie Fiez and Gavin Price for their help-
ful discussion and suggestions on an earlier version of this
Artikel.

Reprint requests should be sent to Xueying Ren, Learning
Research and Development Center, University of Pittsburgh,
3420 Forbes Avenue, Pittsburgh, Pennsylvania 15260, Vereinigt
Zustände, oder per E-Mail: xur1@pitt.edu.

Erklärung zur Datenverfügbarkeit

Materials are available upon request.

Informationen zur Finanzierung

Both authors were supported by funding from the
Nationale Wissenschaftsstiftung (NSF DUE; https://dx.doi
.org/10.13039/100000001), grant number: 1734735; Und
M. E. L. was further supported by a James S. McDonnell
Foundation Scholar Award.

Vielfalt in der Zitierpraxis

Retrospektive Analyse der Zitate in jeder Artikelveröffentlichung-
in dieser Zeitschrift aufgeführt von 2010 Zu 2021 offenbart eine hartnäckige
Muster des Ungleichgewichts zwischen den Geschlechtern: Obwohl die Proportionen von
Autorenteams (kategorisiert nach geschätzter Geschlechtsidentität-
Angabe des Erstautors/Letztautors) Veröffentlichung im Jour-
Abschluss in kognitiver Neurowissenschaft ( JoCN ) während dieser Zeit
waren M(ein)/M = .407, W(Oman)/M = .32, M/W = .115,
und W/ W = .159, die vergleichbaren Proportionen für die arti-
Die von diesen Autorenteams zitierten Elemente waren M/M = .549,
W/M = .257, M/W = .109, und W/ W = .085 (Postle und
Fulvio, JoCN, 34:1, S. 1-3). Folglich, JoCN Ermutigung-
fordert alle Autoren dazu auf, das Geschlechtergleichgewicht explizit zu berücksichtigen
Wählen Sie aus, welche Artikel zitiert werden sollen, und geben Sie ihnen die Möglichkeit-
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Zeitschrift für kognitive Neurowissenschaften

Volumen 35, Nummer 8Identifying the Neural Bases of Math Competence Based on image
Identifying the Neural Bases of Math Competence Based on image

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