Clinical Trial Details
— Status: Enrolling by invitation
Administrative data
NCT number |
NCT04508699 |
Other study ID # |
R21DC018865 |
Secondary ID |
|
Status |
Enrolling by invitation |
Phase |
N/A
|
First received |
|
Last updated |
|
Start date |
October 31, 2024 |
Est. completion date |
July 31, 2025 |
Study information
Verified date |
April 2024 |
Source |
San Diego State University |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Interventional
|
Clinical Trial Summary
School age children with developmental language disorder (DLD) have known semantic learning
deficits but what is less well understood is why semantic learning is difficult for these
children. This project will combine behavioral and brain methods to investigate the cognitive
and linguistic processes underlying semantic learning in children with DLD compared to
typically developing peers. The outcomes will have implications for semantic learning
intervention approaches in DLD.
Description:
This project will elucidate deficits in learning semantic information in developmental
language disorder (DLD, formerly referred to as specific language impairment) by combining
behavioral and neural measures to examine differences in the semantic learning process
between school-age children with and without DLD. Vocabulary knowledge, particularly semantic
knowledge, has a critical influence on reading comprehension and academic success. Despite
the strong association between vocabulary knowledge and academic success, vocabulary is an
under-recognized area of deficit in school-age children with DLD. Younger children with DLD
have well-established deficits in vocabulary and word learning and weaknesses in semantic
knowledge. Additionally, the rate of vocabulary growth in children with DLD decreases
compared to typically developing peers around age 10 and semantic representations of known
vocabulary items are sparse. Even with this knowledge, the field's ability to make progress
toward improved semantic learning in school age DLD is hindered by the lack of basic
information on the underlying nature of the semantic learning deficits in this population.
This project establishes how and why semantic learning differs between school-age children
with and without DLD, providing a much-needed theoretical foundation for clinical research.
Storkel, expanding on an adult word learning model by Leach and Samuel, provides a clearly
testable account of word learning that has been used with children with DLD. This account
involves three processes: 1) triggering, in which a new lexical encounter is compared to
existing lexical representations, 2) configuration, which adds information to the expanding
lexical representation, and 3) engagement, which examines how the new lexical representation
behaves dynamically with existing representations. The configuration process is arguably the
most critical for semantic development. Successful configuration requires the simultaneous
engagement of cognitive and linguistic processes, such as attention, inhibition, working
memory, and semantic and syntactic processing. While it is widely accepted that configuration
is the most affected word learning process in DLD, what is unknown is what underlies deficits
in configuration and whether these deficits vary across the DLD profile. These questions are
further compounded by difficulty measuring configuration and associated processes, given that
they are largely internal, and therefore invisible. Electroencephalography (EEG) addresses
this invisibility problem by allowing for a real-time examination of unconscious levels of
semantic learning and cognitive and linguistic processes. A combined EEG-behavioral methods
approach can illustrate how children with DLD are approaching configuration in terms of the
relative contribution of these processes. The central hypothesis of this research is that
children with DLD engage cognitive and linguistic processes at different points during
configuration compared to their typical peers, resulting in poorer semantic learning
outcomes.
To test the central hypothesis, the investigators will record behavioral and EEG data from
10-12 year old children with DLD and typical-language peers as they complete a semantic
learning task. This age aligns with the point where vocabulary growth rates in DLD further
diverge from typical peers [6]. In the semantic learning task, children listen to sets of
three sentences that all end with the same nonword: half of the sentence triplets support
learning meaning of the nonword, half do not. The investigators will analyze EEG data for
event-related potentials (ERPs) as well as changes in neural oscillations (time frequency
analysis). The investigators will combine EEG and behavioral measures to examine the
following aims:
Aim 1. To investigate the cognitive and linguistic processes underlying configuration in
children with DLD and typical language (TL) peers. This aim will include data from the
semantic learning task. Based on the assessment of behavioral outcomes, the investigators
predict that the TL group will be more accurate in semantic learning than the DLD group. ERP
analyses will focus on the N400 component, associated with semantic processing. Time
frequency analysis will focus on changes in the theta (4-8 Hz) and alpha (8-12 Hz) frequency
bands, typically associated with lexical retrieval and attention/inhibition, respectively.
For both neural measures, the investigators predict engagement of the same components (N400,
theta, alpha) across groups but different patterns of change in those components during
configuration between groups.
Aim 2. To investigate individual differences in configuration in children with DLD and TL
peers. This aim will include data from the semantic learning task and a behavioral assessment
battery. Assessment of behavioral data will focus the types of errors children make during
semantic learning. The investigators expect that children with DLD will provide incorrect
meanings for the nonword that best fit with the first sentence in the triplet and that TL
children will provide incorrect meanings that best fit with the last sentence. The
investigators will also examine individual differences related to semantic learning outcomes
and fine-grained differences in N400 learning effects across groups. Here, the investigators
expect that individual differences in general language ability and semantic knowledge,
measured via the behavioral assessment battery, will be most predictive of both behavioral
semantic learning and N400 change during learning.