Bridging the Divide: Brain and Behavior in Developmental Language Disorder

1. Introduction

Developmental language disorder (DLD) is a heterogenous neurodevelopmental disorder that affects a child’s ability to comprehend and/or produce spoken and/or written language but cannot be attributed to hearing loss or overt neurological damage (coded in the ICD-11 §6A01.2). DLD affects around 7% of children in the US making it more prevalent than other neurodevelopmental disorders, such as autism spectrum disorders (ASD) and dyslexia [1,2,3,4]. Moreover, adults who were diagnosed with DLD as children often experience anxiety and depression and tend to struggle with social relationships, preferring environments and vocations that do not require strong language and literacy skills [5,6]. Despite the prevalence and profound life-long impact DLD can have on a person, little is understood about the neurological basis or etiology of the disorder or how observed language impairments arise.

DLD is typically diagnosed after the age of 4 (around the time a child enters into preschool), when it becomes clear that the child has fallen behind their same age peers in terms of receptive and expressive language skills [7]. Yet, it is likely that the neural substrates underlying the disorder are in place prior to receiving a diagnosis. Current research suggests that some combination of genetic and environmental factors influence neural development in this population, but it is unclear if aberrant brain pathology causes DLD or if DLD leads to altered brain structure and function [8]. Further, there is significant debate regarding theoretical accounts of language impairment patterns observed in children with DLD. To date, none of the neurological or theoretical explanations of DLD fully account for the range of symptoms across individuals or the differing results across research studies [9]. This disconnect has resulted in some researchers defining DLD as a heterogenous disorder that may actually be a spectrum disorder with different phenotypes, like ASD, or may even exist on the same continuum as ASD [10,11,12].

Core to this paper is the notion that the term heterogeneity is oftentimes misused to describe the differences found across studies that are better attributed to differences in research design (population identification, task demands, etc.). For example, if a study uses an assessment that poorly identifies children who have DLD (i.e., low sensitivity) compared to those who do not (i.e., low specificity), that could lead to the DLD group appearing to have more variability in measured behaviors [13,14]. Task demands may also influence how heterogeneous the DLD group appears, especially if the comparison group is poorly matched on age or other criteria. When studies accurately measure known areas of difficulty for children with DLD, such as morphosyntax, they in fact often perform similarly to one another (i.e., heterogeneity is reduced) [14]. In this overview, we broach the topic of heterogeneity briefly to suggest that children with DLD struggle with a range of linguistic and non-linguistic behaviors, but we do so with the knowledge that within specific domains of language they show more consistent impairment patterns than their typically developing peers. outlines language problems commonly reported in children with DLD.

Purpose

The careful characterization of heterogeneity in DLD is important to note because differing patterns of results for both behavioral and neuroimaging studies have obscured what may otherwise be true differences between children with DLD and TD children. As a result, findings have not been synthesized in a way that moves the field forward. As such, though we recognize that there are differences in outcomes across studies, here the aim of this overview is to shift the focus away from addressing outcome differences across studies to identifying converging evidence, so that we can begin to bridge the theoretical and imaging fields. While a few other overviews exist (see [16,17,18]), none that we are aware of link neuroimaging patterns with various theoretical approaches that attempt to capture the range of language impairment patterns found in DLD.

We approach this by first reviewing the underlying neuroimaging patterns to date in DLD (structural (Section 2) and functional (Section 3)). We then summarize a subset of common theoretical accounts of language impairment patterns (across production and comprehension) while attempting to bridge the gap between neuroimaging literature and theory to elucidate brain behavior connections in DLD (Section 5 and Section 6). As will become clear from this overview, the link between theoretical accounts of DLD and findings from neuroimaging research is based on limited evidence; therefore, we will conclude by proposing future directions for neuroimaging research to better understand how aberrant brain structure and function relates to observed language impairments in DLD (Section 7).

2. Structural Neuroimaging Findings in DLD

In this section and in Section 3 below, we describe the structural and functional outcomes of neuroimaging studies as a way to set up the integration of neuroimaging findings in support of theoretical models of language in DLD (Section 5, below).

The link between brain development and language outcomes in children with DLD is unclear, and this lack of connection is apparent when reviewing the DLD neuroimaging literature. Over the past 50 years, there have been fewer than 60 neuroimaging studies (excluding EEG studies) with children diagnosed with DLD. The majority of these studies have focused on structural brain differences when compared to language-unimpaired (neurotypical) children or children with other neurodevelopmental language disorders, such as children diagnosed with ASD and concomitant language impairment. Though there are some consistencies that will be discussed below, it is important to note that the picture portrayed here is tenuous at best due to the limited number of studies that confirm these consistent findings as compared to the larger number of studies that have contrasting results. In this paper, we determined that differences in participant selection and inclusion, diagnostic criteria, methodology, and analyses used underlie the disparate findings to date (see , Table A1). As such, comparing the results across studies and evaluating how structural and functional brain abnormalities contribute to language impairment in children with DLD is challenging. Nonetheless, in this section and in Section 3 below, we provide a general overview of structural and functional neuroimaging findings in DLD and highlight consistent patterns of results. Additionally, when appropriate, we link findings to patterns found with other language-impaired populations to provide credence to the structural and functional patterns found in DLD.

2.1. Structural Brain Differences

Across development, the human brain undergoes a wide variety of structural changes in order to support increasing cognitive demands and the acquisition of new skills [19]. The emergence of white matter pathways in the brain begins in utero following formation of the neural tube and production and migration of neurons [20,21]. The brain then continues to differentiate and refine following birth. Infancy and early childhood are periods of rapid brain development, where the myelination of axons and synaptic reorganization and pruning are abundant in order to strengthen neuronal populations that frequently fire together as well as support those neuronal networks associated with new skills [20,21]. As a result of these changes, the gross structure of the brain continues to visibly change within the first few years of life, with subtle decreases in gray matter volume and increases in white matter volume up until early adulthood [22]. Thus, changes in individual neurons as well as neural networks impact gross brain structure across development.

Any perturbations to the tightly orchestrated processes that contribute to brain development can contribute to a range of developmental disorders [23]. In fact, across different neurodevelopmental disorders, there is widespread evidence of volumetric brain differences compared to neurotypical peers [24,25,26,27,28,29]. For example, individuals with ASD have been shown to have whole and regional brain volume differences when compared to age-matched control children. Lange et al. (2015) found that young children with ASD had larger overall brain volumes than their typically developing peers but as they aged, they showed atypical regional volume decreases [30]. Findings such as these underscore the importance of investigating brain differences between typically and atypically developing populations, so that we can begin to uncover how structural (and functional) alterations contribute to language impairment. In the sections that follow, we provide an overview of structural brain differences in DLD starting with global brain volume, moving to a discussion of gray and white matter volume and integrity. It should be noted that the structure of this overview should not be viewed as an annotated summary of findings, but instead presents thematically related information based on the broader categories that were just described.

2.2. Regional Brain Differences

One of the most consistent findings in children with DLD is anomalous gray matter volume and symmetry within the perisylvian language zone. Though across the DLD literature a number of regions within this zone have been discussed [16,17], this section highlights three specific regions which have consistently been shown to have different characteristics in children with DLD as compared to TD children, namely the planum temporale and inferior frontal gyrus (Figure 1a). In addition to these standard language regions, the other region that will be discussed is the caudate nucleus, as it has been theorized to support speech and language processes (Figure 1b).

Figure 1. Commonly reported regions of interest in studies investigating structural gray matter differences in DLD. (a) The inferior frontal gyrus and the planum temporale. Note that the transparency of the planum temporal is meant to indicate that it is not visible on the lateral surface of the brain. (b) The caudate nucleus located subcortically. Note that it can be further subdivided into the head, body, and tail, but will be discussed as a whole in the text. Figure adapted from Hugh Guiney, CC BY-SA 3.0, via Wikimedia Commons https://en.m.wikipedia.org/wiki/File:Human-brain.SVG (accessed on 12 September 2023).

2.3. White Matter Pathways

Historically, investigations into brain function have focused on the size and engagement of cortical and subcortical gray matter regions of the brain. However, a missing piece of those investigations are the connections that allow gray matter regions to coordinate activity. Since we know brain regions do not operate in silos, recent technological advances have provided scientists a way to measure the important connections that exist between gray matter regions, known as white matter, which comprises the structural wiring of the brain (Figure 3). Due to the early development of the brain, white matter pathways are already present by 30 weeks of gestation [77]. However, between birth and two years of age, children undergo a period of rapid brain development, highly influenced by genes and the environment, that helps further shape the neural architecture of the brain [20,78]. As children continue to develop, white matter volume continues to increase until around the fourth decade of life to support improvements in cognitive skills [19,79,80].

Figure 3. Example of white matter pathways connecting gray matter regions in the left hemisphere.

There is a strong relationship between neural activity associated with new skills and the formation of efficient, myelinated white matter pathways that connect gray matter regions throughout the brain [19]. As a result of this critical interaction, any deviation in the typical formation of white matter pathways will likely contribute to functional impairments [20]. Prior research has demonstrated a link between white matter alterations and language impairment in children with a range of neurodevelopmental disorders, including autism spectrum disorder, dyslexia and other reading disorders, and epilepsy [81,82,83,84]. While research on white matter connectivity in children with DLD is limited, like other neurodevelopmental populations, there seems to be a connection between language impairment and altered white matter volume and diffusivity (movement of water molecules along white matter pathways). Here, we provide an overview of these findings starting with white matter volume changes in DLD as compared to TD and then we turn attention to the diffusivity of white matter tracts involved in language processing within dorsal and ventral regions.

3. Functional Neuroimaging Findings in DLD

Functional magnetic resonance imaging (fMRI) provides researchers (and clinicians) a glimpse into the window of the active brain. The examination of brain activity during specific tasks can inform theories of behavior, in this case, language.

3.1. Functional Magnetic Resonance Imaging (fMRI) Studies

Studies investigating functional brain activity in children with DLD using fMRI vary significantly across the tasks that are used and the age ranges studied. However, the general picture suggests that children with DLD differ across activation levels (hypo- and hyper-activation), locations, and laterality patterns of brain activation when compared to typically developing children during language-related tasks. Since fMRI results are largely based on task demands, it is important to understand that different tasks will produce different patterns of activation. However, the goal here is to highlight consistent patterns of regional activation differences across the broader categories of expressive and receptive tasks (though see for a more detailed review of activation patterns across specific tasks). As will become clear from the discussion below, more studies are needed to not only verify results (particularly across a wider range of tasks) but to also explore whether differences in the level, location, or laterality of activation patterns are due to other factors such as maturational changes (as discussed in prior sections).

3.2. Cerebral Blood Flow Patterns

There is a tight coupling between neuronal activity and increased blood flow to active brain regions (e.g., neurovascular coupling). When neurons are active, they send chemical and electrical signals to blood vessels to dilate which in turn increases the flow of blood and allows an abundance of oxygen and glucose to be delivered to neurons as a fuel source. As neurons consume the influx of oxygen, the blood becomes deoxygenated. It is this change in oxygen content on which the fMRI or blood oxygen level dependent (BOLD) signal is based [105]. However, if the relationship between neural activity and the BOLD response is not accurately modeled via the hemodynamic response function (HRF), then activation patterns may be misinterpreted. Prior research has revealed that the HRF may deviate from the canonical pattern with clinical populations [106]. For example, following a stroke, the amount of time it takes for blood to perfuse neural tissue (i.e., transit delay time) may be longer than normal and the amount of blood that gets delivered to neural tissue may be reduced [99,107,108]. Thus, when conducting fMRI studies with suspected neurologically compromised populations, including neurodevelopmental groups, it is critical that the modeled HRF reflects the true nature of blood flow in the brain in order to extract accurate BOLD signal estimates.

4. Interim Summary: Neuroimaging Patterns in DLD

Thus far, we have highlighted similarities as to the structural and functional differences in DLD as compared to TD across neuroimaging studies, while acknowledging that findings are based on limited evidence and often have contrasting results (likely due to methodological differences). We approached this portion of the overview by reporting converging evidence across structural (whole brain, gray matter, white matter, etc.) and functional measures (task-based activation and cerebral blood flow). The current state of the DLD neuroimaging literature suggests that structurally, when compared to typically developing children, children with DLD have smaller overall brain sizes, they show differences in whole brain and regional gray and white matter volume (potentially mediated by age), and they have altered white matter macro- and micro-structure of language-related tracts. Functionally, they demonstrate differences in the level of brain activation (i.e., hypo- and hyper-activation), the location of activation (i.e., regional differences), and laterality of activation (i.e., left vs. right hemisphere recruitment). They also show differences in the level and lateralization patterns of cerebral blood flow.

Importantly, while using a different approach, we identified similar regions of altered brain structure and function (planum temporale, inferior frontal gyrus, and caudate nucleus) to a prior systematic review of neuroimaging studies in DLD conducted by Mayes and colleagues (2015) [16]. While it is clear from both this overview and the review conducted by Mayes et al. (2015) that altered brain structure and function are important components of DLD pathology, the connection between neuroimaging findings and observed language deficits has been less apparent, as research findings from neuroimaging studies are often reported independently of theoretical research findings. Therefore, it is our goal in this paper to begin to make those connections so that moving forward as a field we can design more theoretically informed neuroimaging studies with more sophisticated linguistic material to better tease apart how aberrant brain structure and function relates to the range of reported language impairments in DLD.

In the next section, we briefly review selected theoretical accounts of DLD, and after each section, we comment on the potential link between the proposed theoretical account and the neuroimaging findings discussed above in an attempt to elucidate brain–behavior relationships.

5. Neuroimaging Evidence Supporting Theoretical Accounts of Language Impairment Patterns in DLD

In order to illustrate how neuroimaging evidence can better inform theoretical accounts of language impairment patterns in DLD, we now discuss some common theories that have been proposed to explain language impairment patterns in DLD.

Across the published studies, there are a range of theoretical accounts that attempt to describe and explain observable language error patterns in children with DLD. While errors are part of typical language development (overgeneralizations, pronoun resolution, etc.), here we refer to error patterns that do not resolve with time [122]. Theories outlining these error patterns can generally be divided into three well represented arguments in the literature that point to deficits specific to (1) linguistic knowledge (e.g., morphosyntax), (2) domain general language processing (e.g., phonological working memory, speech perception, etc.), or (3) non-linguistic cognitive processes associated with language (e.g., working memory, processing speed, etc.) [9]. In isolation, each of these theoretical approaches have merit; however, children with DLD can exhibit both linguistic and non-linguistic impairments, making it difficult to propose a single comprehensive and encompassing theory that can account for hallmark deficits, such as difficulty with grammar, while also explaining other less consistent findings, such as deficits in attention and speed of processing. It is our hope that by making connections between theoretical accounts of DLD and the more consistent neuroimaging findings, that we can point to potential areas to focus future research endeavors. To accomplish these goals, below we present three theoretical accounts of language impairment in DLD. After each account, we integrate the imaging findings described above so as to build a bridge between two seemingly disparate areas.

5.1. Theoretical Approach: Linguistic Knowledge

Deficits in morphology and syntax (i.e., morphosyntax) are ubiquitous in children diagnosed with DLD. These observations have led to proposals suggesting that language deficits stem from limitations in linguistic knowledge (e.g., tense marking rules, phrase structure rules, movement, etc.; . One of the earliest among the agreement, tense, and number marking accounts is the Extended Optional Infinitive (EOI) account [123]. According to Wexler (1994), around 4–5 years of age, typically developing children undergo a stage by which they optionally mark the tense and number on finite verbs in main clauses (e.g., she drinks coffee can optionally be *she drink coffee; the asterisk represents an ungrammatical utterance) [124]. In cases where tense/number is not marked, children tend to produce the infinitive form of the verb (i.e., drink). Building off of Wexler’s account, Rice et al. (1995) suggested that children with DLD remain in this optional infinitive stage for a period of time that extends beyond that of a typically developing child, before their grammatical usage catches up to that of an adult (if it does at all; see [125]).

The EOI account works well to characterize error patterns made by children diagnosed with DLD when speaking languages such as English and French, but it fails to account for the grammatical errors made by children who use other languages such as Italian and Spanish due to cross-linguistic differences in syntactic structure. Thus, a number of theoretical accounts followed which expanded on the EOI premise that children with DLD struggle with aspects of tense and agreement (see [125,126,127]). However, evidence has shown that children with DLD are not limited to errors in just tense and/or agreement as these accounts suggest, and thus, this theory underspecifies observed errors. To address this issue of limited scope, other accounts focused on structural complexity, such as the Representational Deficit for Dependent Relations (RDDR), suggesting that deficits stem from difficulty with performing these complex operations. One example would be the movement of a wh-question word (e.g., who) to the front of a sentence to form a question [122,128,129]. Though accounts such as the RDDR cover a wider range of deficits, especially across languages, they lack specificity, particularly concerning how and in which instances children with DLD struggle with complex operations. Further, the connection between deficits across different linguistic domains (i.e., syntax, morphology, and phonology) needs clarification since it is unclear how deficits in processing complex syntactic structures would also result in other deficits discussed in the sections below.

Other theories that focus on the application of rules, like the Narrow Rule Learning account [130], posit that children with DLD tend to stick to structures that have a high number of exemplars in the language, which may help bridge theoretical gaps, but no single linguistic theory can explain all of the inconsistencies in deficits across linguistic domains [122]. In addition, the majority of linguistic accounts have focused on observable production errors in individuals with DLD, which limits the ability to generalize to language deficits as a whole.

5.2. Theoretical Approach: Language Processing Accounts

Linguistic-based accounts of DLD, such as those described above, benefit from being able to explain specific error patterns in language use; however, they often fail to encompass the wide range of deficits across languages. Further, they may not accurately reflect how a child with DLD processes (as opposed to produces) language. Therefore, some theories posit that language impairment stems from aberrant processing in domain general language systems that impact language use . Among these theories are those that suggest that children with DLD have general auditory processing deficits. In a seminal series of studies in the 1970s, Tallal and Piercy [136,137], found that children with language impairment (those diagnosed with DLD and those with hearing impairments) performed worse than TD control children on a variety of tone and speech perception tasks that included categorization, discrimination, and temporal sequencing of auditory information. Children with DLD in particular seemed to struggle with auditory stimuli that were presented briefly or in rapid succession, which led the authors to suggest that the source of deficit stems from difficulties with temporal processing at the phoneme level.

In an attempt to expand on Tallal and colleague’s supposition of an underlying temporal processing deficit, Schwartz, Scheffler, and Lopez (2013) argued that children with DLD have deficits in perceptual processing, specifically, categorical perception and the use of perceptual speech cues, which in turn affects storage and access to lexical representations [138]. Though these perceptual accounts can be compelling, one point of weakness is in describing how they can lead to disruptions of grammatical processing as opposed to just lexical or phonological processing. Leonard, McGregor, and Allen (1992) attempted to address this limitation by proposing the Surface Account, which posited that the complex operations required to process grammatical markers taxes the system resulting in incomplete processing of grammatical morphemes [139]. It is argued that this difficulty is then further amplified by the low perceptual saliency of grammatical morphemes, which increases the amount of exposure needed to learn them. However, evidence in support of this theory is mixed (see [122] for a review); thus, as it stands, more work needs to be carried out to understand if and how these processing differences result in the range of deficits observed.

Phonological short-term memory (also referred to as phonological working memory) is another commonly cited source of language difficulty for children with DLD. Different models of how language is stored have been proposed, but the most prominent view of phonological short-term memory comes from Baddeley and colleagues [140,141,142]. Based on the model, Gathercole and Baddeley (1989) proposed that phonological short-term memory supports vocabulary development by helping to form stable phonological representations [143]. This proposal was supported by findings that demonstrated a relationship between poor nonword repetition skills and smaller vocabularies in children with DLD, indicating that they struggle to form and store stable phonological representations [144]. Thus, nonword repetition tasks have become a consistent and reliable measure for characterizing what many have suggested are phonological short-term memory impairments in children with DLD, but again, these theories are unable to account for observable impairments at the syntactic level during production and they tend to lack explanatory power for comprehension impairments [145]. Though there are processing theories that extend beyond the phoneme and word-level, the majority of studies have focused on this level. Thus, to better account for the full range of deficits observed in children with DLD, it is suggested that researchers look to other processing theories that have been proposed for other language-impaired populations as they may inform a larger range of observed deficits [146,147].

5.3. Theoretical Approach: Non-Linguistic Cognitive Processing

The notion that children with DLD have broader processing limitations beyond language has received considerable attention in the DLD literature . In looking at the literature from other well-studied language-impaired populations, namely individuals with aphasia, it has been proposed that linguistic knowledge remains intact in these individuals, but the ability to tap into the cognitive resources necessary to build the representations or perform complex operations with them are impaired (though see [151] for an opposing view) [152]. Studies investigating these limitations generally focus on aspects of working memory, such as resource allocation capacity and attention. Within the DLD literature, similar proposals have been investigated.

For example, Montgomery (2000) studied the ability of children with DLD to allocate resources during a word recall task [153]. Based on the assumptions of the working memory model proposed by Just and Carpenter (1992), the premise of their study was that people have a limited pool of resources by which they can process and store incoming information and children with DLD have even greater reductions in processing capacity [154]. They found that children with DLD could complete simple processing tasks, but as the task complexity increased (i.e., dual-load tasks where participants had to sort items by size and semantic category), performance worsened compared to controls, suggesting that children with DLD have reduced resources (e.g., processing capacities), which constrained the ability to successfully perform multiple, simultaneous cognitive operations. One big limitation of processing capacity theories is that it is not always clear what the “resource” being allocated is, though it is often conceived as an attentional one [141]. The problem with this is that children with DLD do not always demonstrate impairments in attention, or if they do, it tends to be situational. Further, it is challenging to design studies that can be falsified given that processing capacity is difficult to distinguish and measure.

Other non-linguistic cognitive theories have suggested that children with DLD are slower to perform a wide range of linguistic and non-linguistic tasks based on evidence from reaction time studies [122]. One early account for reduced processing speeds was the Generalized Slowing Hypothesis, which posits that children with DLD perform more slowly on both linguistic and non-linguistic tasks when compared to typically developing peers, and this difference is proportional to the complexity (i.e., the number of operations) required by the task [155]. Like with attentional studies, the problem with this hypothesis is that not all children with DLD show slowed reaction times on non-linguistic tasks, and those that do seem to do so selectively across different tasks [156]. Therefore, more work is needed to understand what causes some children to have slower processing speeds than others, as well as whether it affects broader, domain general neural systems or more specific ones.

One final group of domain-general cognitive theories discussed here is related to impaired learning systems [72]. The most comprehensive account of learning deficits in DLD is the Procedural Deficit Hypothesis (PDH) [73]. According to the PDH, children with DLD have a deficit in the procedural memory system. This system is comprised of frontal and subcortical brain regions that support the learning of linguistic, motor, and other non-linguistic sequences. The PDH posits that aberrant function of the procedural system can lead to impairments in any of the systems associated with it; thus, the PDH attempts to account for the wide range of linguistic and non-linguistic impairments reported in the DLD literature. It also makes a distinction between procedural and declarative systems. The procedural system is thought to underlie the acquisition and use of rule-governed grammatical computations, while the declarative system is thought to underlie storage of lexical knowledge, such as semantic features and the memorization of more arbitrary, word-specific information like irregular past-tense verb forms. Research investigating sequence-based procedural learning tasks has found that children with DLD demonstrate impairments in this realm but are less likely to show deficits in non-sequenced based declarative tasks [72]. However, Ullman and Pierpoint (2005) point out in their model that the procedural and declarative systems are not entirely independent of one another due to their connections with other systems, such as those involved in working memory, so it is possible for children with DLD to show deficits in processes supported by both systems [73]. This becomes particularly relevant when trying to account for smaller vocabularies in children with DLD, since the declarative system likely supports this process.

Though the PDH provides a nice framework for capturing one of the most striking impairments in DLD (grammatical errors), more work needs to be done to refine the hypothesis, particularly in terms of the functional boundaries between the procedural and declarative systems. Further, this model provides one of the most comprehensive accounts for the varied neuroimaging findings, but in an effort to account for such a broad range of deficits, the model posits that any deficit can be explained by the procedural system or connections to it, with limited evidence to support these claims.

5.4. Interim Summary: Linking Theory to Brain

There are a number of different accounts that have been proposed in an attempt to explain patterns of language impairment in children with DLD. We divided these theories into three well-represented categories, namely deficits in linguistic knowledge, language processing, and non-linguistic cognitive processing, and briefly discussed evidence supporting each. Each of these theories does well at capturing specific measured behaviors that differ in DLD compared to typically developing peers, but when zooming out more broadly to examine the range of behaviors reported (e.g., tense omission, poor phonological awareness, slower processing speed, etc.), there are a number of competing accounts that in isolation are not able to explain the broad scope of impairments in DLD. On one end of the theoretical continuum, the linguistic accounts lack explanatory power and are too narrow in scope, especially since most tend to focus on word-level, production errors; and on the other end, the more general non-linguistic processing accounts are too broad, as they cannot explain why language (and grammar) is affected above and beyond other cognitive systems in DLD if the deficit is in a domain-general system. One possibility is that DLD is a spectrum disorder (like autism) with different phenotypes [11]. If this is the case, neuroimaging evidence could help with the identification of different phenotypic groups by linking specific neural profiles to behavioral patterns. This would require much larger datasets than are typical in DLD research and would be most informative with longitudinal designs.

Alternatively, another approach could be to examine language abilities that are more commonly affected in children with DLD as a starting point to elucidate the relationship between altered brain structure/function and variable language profiles. For example, difficulty with the use of grammatical features of language and poor performance on nonword repetition tasks are common in DLD. Both of these abilities in typically developing individuals have been linked to the inferior frontal gyrus (IFG) [43,130,131]. In a recent study by Bahar and colleagues (2023) with children with DLD, the authors found that performance on a nonword repetition task was a significant predictor of surface area within the left IFG [41,46,133,134]. It may be the case that alterations within the left IFG are linked to difficulties with both of these language tasks. Further, given that the IFG is a densely connected hub within the language network, abnormal development of the region could impact other connected regions, such as the superior temporal sulcus. This would help explain empirical evidence indicating that language deficits exist across different language domains, as certain deficits could be linked back to a common neural substrate, and variations beyond those deficits could potentially be linked to alterations in structural and functional connections with other regions.

One other possibility is that the underlying neurobiological mechanisms supporting brain development are impacted in DLD. Recent work from Bahar et al. (2023) and Krishnan et al. (2022) is beginning to explore this area by investigating the underlying properties of brain tissue [41,74]. For example, as mentioned previously, Krishnan et al. (2022) measured gray matter myelin content in children and adolescence with DLD and found less myelin in the caudate nucleus and left inferior frontal gyrus. Together, these brain regions partially comprise the corticostriatal loop, which plays a role in sequential learning tasks such as learning the grammatical rules of a language. Based on their findings, the authors posited that children with DLD have difficulty learning the complex rules of language, as evinced by altered myelin content within this language learning circuitry (though they do not speculate on the causal direction of the relationship between reduced myelin and language abilities). Typical myelin development begins in utero and is subject to a number of carefully timed genetic processes that slowly give way to more environmental influences postnatally [76]. Thus, a reduction in myelin content could be related to specific genetic markers early on in DLD. Then, as a child continues to develop, experience could have a greater impact on the development of higher-order cognitive functions, like language learning. This idea is supported by the fact that myelination within the brain regions associated with these higher-order functions takes longer in comparison to sensory and motor regions to develop. This could help provide support for more domain-general theories, like the Procedural Deficit Hypothesis or the Generalized Slowing Hypothesis mentioned in Section 5.3 and Section 5.4. Unlike sensory and motor cortices, the brain regions involved with language processes take longer to “mature”; thus, early alterations to the underlying properties supporting brain function could have a detrimental impact on later-developing language network architecture, thus resulting in the range of language impairments that we observe in DLD.

6. Discussion: The Current State (and Limitations) in Linking Theory to Brain

It is clear that more work needs to be conducted to obtain a better sense of the neural organization and networks engaged in language processes in children diagnosed with DLD. Overall, the current state of the field suggests that children with DLD have atypical brain volume, laterality, and activation/connectivity patterns compared to their neurotypical peers. Behaviorally, one of the most striking impairments is in the production of grammatical morphemes, but research has demonstrated impairments in a range of other linguistic and non-linguistic tasks, such as vocabulary development, nonword repetition, and short-term memory (though it should be noted that it is difficult to create purely non-verbal short-term memory tasks). In terms of neuroimaging research, across both structural and functional brain studies, the planum temporale (located in the posterior superior temporal gyrus), the inferior frontal gyrus, and the caudate nucleus consistently show altered patterns in children with DLD when compared to typically developing children. Taken together, it is reasonable to speculate that atypical language development in DLD is not an environmental side effect but is in fact related to anomalous development of brain structures and/or function. However, few studies have attempted to link theoretical accounts of language impairment in DLD to neuroimaging findings, resulting in two disparate bodies of literature.

The goal of this overview was to synthesize the literature in a way that could help lay the groundwork for more theoretically motivated neuroimaging research. Though the connections made here are purely speculative based on the evidence presented, they do align well with the recent literature demonstrating structural and functional differences in the corticostriatal pathways that support language learning [41,72,74,159]. These recent studies utilize more consistent methodological approaches to make connections between altered brain structure and theoretical accounts of DLD, and they explore the underlying properties that support broader measures of structural brain development beyond brain volume (i.e., myelin, surface area, cortical thickness, etc.). This is important because prior neuroimaging research with children with DLD rarely overlaps in methodological approaches and often focuses on these gross brain measures, making it difficult to draw conclusions about the underlying contributors of altered brain structure or function in DLD. At this point, more research with larger sample sizes and carefully defined participant criteria is needed to replicate previous neuroimaging findings and build on our current knowledge of language impairment patterns in DLD. By laying a foundation for more theoretically motivated neuroimaging research, we may be able to better link neural differences to specific language profiles that align with current theoretical accounts of language deficits in DLD.

7. Future Directions: New Approaches in Linking Theory to Brain

There were a number of ways to craft this overview. We chose to categorize aspects of studies based on a variety of measures used to evaluate brain structure (i.e., whole brain volume, gray matter volume, white matter diffusivity, etc.) and brain function (i.e., task-based activation and cerebral blood flow). Our goal was to present converging evidence about neural regions implicated in DLD in order to link theory to brain. Importantly, using this approach, we found a similar pattern of results to a systematic review conducted by Mayes and colleagues (2015) in which they divided neuroimaging studies in DLD by the methodology employed (e.g., semi-automatic morphometry, voxel-based morphometry, etc.). Similarly to our findings, Mayes et al. (2015) reported that the posterior superior temporal gyrus, the inferior frontal gyrus, and the caudate are implicated in DLD pathology [16]. Given the consistencies across these reviews and more recent studies in DLD, future research will benefit from exploring the relationship between altered structure/function of these brain regions and performance on language tasks that are linked to activation within those regions.

A problem discussed throughout this overview, though, is the lack of consistency across studies in terms or the direction of volumetric differences (e.g., larger in the left hemisphere, smaller in the right, etc.) and the location and level of brain activation in the three brain regions frequently implicated in language impairment in DLD. One possibility is that broad measures of brain structure (i.e., volume) and brain function may overshadow important underlying properties that contribute to these gross measures. For example, as previously mentioned, Bahar et al. (2023) demonstrated that altered brain volume in DLD was largely driven by differences in surface area rather than cortical thickness, which is noteworthy as surface area and cortical thickness follow distinct developmental trajectories and are likely driven by distinct neurobiological mechanisms [41]. By exploring the underlying properties that index developmental changes, it can help to better account for inconsistent findings within the literature.

To date, one area of neuroimaging research that has received little attention is the processes underlying neuronal function and connectivity, particularly within the language network. Research from another language-impaired population, individuals with aphasia, has demonstrated that reduced cerebral blood flow to middle temporal regions correlates with auditory comprehension impairments, despite these regions appearing structurally uncompromised by the lesion [99]. It may be the case that important language regions, such as the left inferior frontal network, are hindered during development due to aberrant blood flow, underconnectivity, etc., resulting in those consistently observed morphosyntactic deficits in DLD. By examining distributed networks and the processes that underlie neuronal function (e.g., cerebral blood flow, glucose metabolism, etc.) in children with DLD (as compared to neurotypically developing children) it may help to further bridge theoretical accounts as well as aid in the identification of potential biomarkers of DLD.

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