Predicting Language and Literacy Growth in Children With ASD Using Statistical Learning (PLAUSL)

The goal of this observational study is to test a reciprocal relationship between statistical learning and the development of language and literacy in first-graders with autism and their non-autistic peers. The main questions it aims to answer are:

1. whether children's statistical learning abilities can predict their long-term improvement of language and literacy skills in school;
2. how children's brains automatically learn patterns from speech and prints;
3. whether children's learning in the lab reflects the language patterns they have learned over the years from their native language.

First-grade students will participate in the study twice across three months.

During Time 1, children will complete

• a battery of language, reading, and cognitive assessments
• a series of computer-based statistical learning games both inside and outside of functional MRI scanner.

During Time 2, children will complete a battery of language and reading assessments to detect the growth in three months.

Researchers will compare the autistic and the non-autistic groups to see if statistical learning plays a similar or different role in predicting children's language and literacy growth.

Study Overview

• Status: Recruiting
• Conditions: Autism Spectrum Disorder; Language Development; Literacy
• Intervention / Treatment: Behavioral: Structural vs. Random sequences of stimuli; Behavioral: Intact vs. Degraded speech; Behavioral: Repeating 5-syllable nonwords or 2-syllable nonwords; Behavioral: Recall letter or syllable strings that either contain highly frequent bigram/trigram items or infrequent items according to English Corpus data.

Detailed Description

In this one-year R56 project, the investigators will design a mini-longitudinal project to test how children's linguistic statistical learning predicts their growth in spoken and written language in three months. The investigators predict that weaker linguistic statistical learning underlies the exacerbation of language and literacy delay in children with ASD. In this project, the investigators will gamify an existing study protocol, which has led to promising discoveries motivating the original R01 proposal. With an enriched platform, the investigators expect a low attrition rate (<10%) and few missing data (>90% task completion rate) in this longitudinal sample of six-year-olds both with and without ASD, matched on age and sex ratio. This project will lead to critical feasibility and preliminary data to support the key hypotheses of all three aims. Due to a shortened timeline, the investigators plan to recruit 25 children per group, sampled from a diverse population, to ensure the team's capacity for a three-month follow-up session before the end of a school year.

Aim 1: Establish the longitudinal relationships between SL and language/literacy development in both TD and ASD. In this aim, the investigators will evaluate the role of SL in both concurrent and the growth of language/literacy skills assessed in three months during the school year. The investigators expect that linguistic SL (embedded pattern learning of syllables and letters) is associated with concurrent language and literacy skills and will prove to be crucial predictors of growth in language and literacy skills in both groups. Given the small sample size in this one-year project, the investigators will build prediction models both across the two groups and within each group. Sex, concurrent language and reading skills, and nonverbal IQ will be included as key covariables.

Aim 2: Determine the longitudinal relationships between neural bases of SL and developing language networks in the brains of children with ASD. In this aim, the investigators will test whether altered brain functions during linguistic SL are related to poorly functioning language networks (language comprehension and phonological working memory), defined within each individual child. The investigators will also examine whether neural bases of linguistic SL predict future language and literacy development in children with ASD. Participants from Aim 1 will complete linguistic SL tasks and two well-validated language tasks in the fMRI scanner at Time 1. The investigators expect to find reduced engagement of language networks during SL in children with ASD and to provide a neurobiological explanation for the cascading effect impaired linguistic SL casts on future language development.

Aim 3: Test whether linguistic SL is a proxy for children's sensitivity to real-world language statistics. The investigators ask whether the specific weakness in linguistic SL in ASD generalizes to real-world language statistics using serial recall tasks, a well-validated instrument for individuals' sensitivity to native-language statistical patterns. The same participants in the previous aims will complete a phonological and an orthographic serial recall task containing both high- and low-frequency English bigrams/trigrams at Time 1 and Time 2. The investigators predict that these tasks will be associated with children's linguistic SL performance in Aim 1 and children's linguistic SL at earlier times will predict the future growth of sensitivity to natural language statistics. These findings will establish the critical link between linguistic SL and children's natural language skills.

• Study Type: Observational
• Enrollment (Estimated): 50

Contacts and Locations

• Study Contact: Name: Brynn Siles, BS; Phone Number: 617-830-1530; Email: plausl.project@gmail.com

Study Locations

• United States
  • Massachusetts
    • Boston, Massachusetts, United States, 02115
      • Recruiting
      • Northeastern University
      • Contact: Brynn Siles, BS; Phone Number: 617-830-1530; Email: plausl.project@gmail.com
      • Principal Investigator: Zhenghan Qi, MD/PhD
    • Boston, Massachusetts, United States, 02215
      • Enrolling by invitation
      • Boston University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

The study population will be a community sample from the Greater Boston Metropolitan Area.

Description

Inclusion Criteria:

• current first grader (6;0 - 7;6)
• Geographically located within the Greater Boston Metropolitan Area
• Native English speakers
• Right-handed
• Normal hearing
• Normal or corrected vision

Inclusion criteria for children with ASD (N = 25):

• Children with a professional diagnosis of autism according to expert clinical judgment
• Capable of speaking sentences with three or more words
• Social Communication Parent Questionnaire score > 15
• Autism diagnosis confirmed by ADOS

Inclusion criteria for typically developing controls (N = 25):

• Neurotypical: with no known cognitive, neurological, or psychiatric disorders
• Social Communication Parent Questionnaire score < 11
• Receive a score within 1 SD of the population mean for age on all assessments.

Exclusion Criteria:

• left-handed
• non-native speakers of English
• More than 30 hours of exposure to a language other than English per week
• history of brain injuries and head injuries
• intellectual disability, mutism, motor delay, or developmental coordination disorder
• metal in body
• claustrophobic
• history of prior neurosurgical procedure
• substance abuse
• signs of increased intracranial pressure

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Children with ASD; Structural vs. Random sequences of stimuli; Intact vs. Degraded speech; Repeating 5-syllable nonwords or 2-syllable nonwords; Recall letter or syllable strings that either contain highly frequent bigram/trigram items or infrequent items according to English Corpus data	Behavioral: Structural vs. Random sequences of stimuli; Participants will see or listen to sequences of sounds and images either in a structured condition or a random condition.; Behavioral: Intact vs. Degraded speech; Participants will listen to an audiobook "Alice in Wonderland" in the MRI scanner. The speech is either intact or degraded.; Behavioral: Repeating 5-syllable nonwords or 2-syllable nonwords; Participants will listen to nonwords (either 5-syllable or 2-syllable) and then repeat them as accurately as possible in the MRI scanner.; Behavioral: Recall letter or syllable strings that either contain highly frequent bigram/trigram items or infrequent items according to English Corpus data.; Participants will read (orthographic serial recall) or listen to (phonological serial recall) strings.
Typically Developing Children; Structural vs. Random sequences of stimuli; Intact vs. Degraded speech; Repeating 5-syllable nonwords or 2-syllable nonwords; Recall letter or syllable strings that either contain highly frequent bigram/trigram items or infrequent items according to English Corpus data	Behavioral: Structural vs. Random sequences of stimuli; Participants will see or listen to sequences of sounds and images either in a structured condition or a random condition.; Behavioral: Intact vs. Degraded speech; Participants will listen to an audiobook "Alice in Wonderland" in the MRI scanner. The speech is either intact or degraded.; Behavioral: Repeating 5-syllable nonwords or 2-syllable nonwords; Participants will listen to nonwords (either 5-syllable or 2-syllable) and then repeat them as accurately as possible in the MRI scanner.; Behavioral: Recall letter or syllable strings that either contain highly frequent bigram/trigram items or infrequent items according to English Corpus data.; Participants will read (orthographic serial recall) or listen to (phonological serial recall) strings.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Language Composite Score	The concurrent language skills will be computed by averaging each child's standardized scores across the TILLS language subsets and NIH Toolbox Picture Vocab. The composite score will range between 50 and 150. The higher score, the better language skills.	At each of the two measuring time points during the school year of the first grade with three months apart over a 40-minute teleassessment session
Reading Composite Score	The concurrent reading skills will be computed by averaging across WRMT-III subtests, NIH Toolbox oral reading recognition. The composite score will range between 50 and 150. The higher score, the better reading skills.	At each of the two measuring time points during the school year of the first grade with three months apart over a 40-minute teleassessment session
Language/Literacy Disorder Status	The binary status is determined by children's TILLS Identification core scores	At the first visit during the school year of the first grade over a 40-minute teleassessment session
Neural similarity between language and statistical learning tasks	The multivoxel similarity between language processing (or phonological working memory) tasks and the statistical learning task in subject-specific language (or phonological working memory) brain regions.	At the first visit during the school year of the first grade over a two-hour MRI session
Orthographic statistical sensitivity	The difference in recall accuracy of bigrams/trigrams between the high- vs. low-frequency items in the orthographic serial recall task.	At each of the two measuring time points during the school year of the first grade with three months apart
Phonological statistical sensitivity	The difference in recall accuracy of bigrams/trigrams between the high- vs. low-frequency items in the phonological serial recall task.	At each of the two measuring time points during the school year of the first grade with three months apart

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Linguistic SL composite scores	The linguistic SL score will be the average of the letter and the syllable composite scores after averaging across the normalized scores of RT slope, accuracy, and serial recall.	At each of the two measuring time points during the school year of the first grade with three months apart
Neural sensitivity to statistical regularities during the statistical learning tasks	The magnitude of BOLD responses to structured vs. random conditions in the syllable and the letter statistical learning tasks.	At the first visit during the school year of the first grade over a two-hour MRI session

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Neural activation to statistical regularities during the language tasks	The magnitude of BOLD responses to intact vs. degraded speech and 5-vs-2 syllable in the language and phonological working memory tasks	At the first visit during the school year of the first grade over a two-hour MRI session

Collaborators and Investigators

• Sponsor: Northeastern University
• Collaborators: Boston University; University of Delaware; Cornell University
• Investigators: Principal Investigator: Zhenghan Qi, MD/PhD, Northeastern University

Publications and helpful links

General Publications

• Ozernov-Palchik O, Qi Z, Beach SD, Gabrieli JDE. Intact procedural memory and impaired auditory statistical learning in adults with dyslexia. Neuropsychologia. 2023 Sep 9;188:108638. doi: 10.1016/j.neuropsychologia.2023.108638. Epub 2023 Jul 28.
• Hu A, Kozloff V, Owen Van Horne A, Chugani D, Qi Z. Dissociation Between Linguistic and Nonlinguistic Statistical Learning in Children with Autism. J Autism Dev Disord. 2023 Feb 7:10.1007/s10803-023-05902-1. doi: 10.1007/s10803-023-05902-1. Online ahead of print.
• O'Brien AM, Perrachione TK, Wisman Weil L, Sanchez Araujo Y, Halverson K, Harris A, Ostrovskaya I, Kjelgaard M, Kenneth Wexler, Tager-Flusberg H, Gabrieli JDE, Qi Z. Altered engagement of the speech motor network is associated with reduced phonological working memory in autism. Neuroimage Clin. 2023;37:103299. doi: 10.1016/j.nicl.2022.103299. Epub 2022 Dec 23.
• Schneider JM, Hu A, Legault J, Qi Z. Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques. J Vis Exp. 2020 Jun 30;(160):10.3791/61474. doi: 10.3791/61474.

Helpful Links

• PLAUSL Project Website

Study record dates

Study Major Dates

• Study Start (Actual): January 13, 2024
• Primary Completion (Estimated): August 31, 2024
• Study Completion (Estimated): August 31, 2024

Study Registration Dates

• First Submitted: March 5, 2024
• First Submitted That Met QC Criteria: March 20, 2024
• First Posted (Actual): March 27, 2024

Study Record Updates

• Last Update Posted (Actual): March 27, 2024
• Last Update Submitted That Met QC Criteria: March 20, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Keywords: fMRI; Autism Spectrum Disorder; Neuroimaging; Implicit Learning; Language Development; Statistical Learning; Reading Development
• Additional Relevant MeSH Terms: Mental Disorders; Neurodevelopmental Disorders; Child Development Disorders, Pervasive; Autism Spectrum Disorder
• Other Study ID Numbers: 1R56DC020208-01A1 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The demographic and behavioral information will be made available in the form of a comma delimited text file and a code book. We will disguise individuals' identities by redacting birthdates and date of examination from the available records and by replacing names with coded alphanumeric values. When permission is granted, we will deposit all raw, de-identified behavioral and neuroimaging data into the National Institute of Mental Health Data Archive (NDA). The fMRI data will be uploaded to a public repository to improve meta-analyses and data-sharing (NeuroVault, OpenNeuro). To protect the privacy and confidentiality of the participants, raw video or audio files will not be shared outside of the research team. We do not commit to processing additional data beyond our own needs for the purpose of sharing. Any data we have processed we are willing to share. We will submit electronic versions of all accepted manuscripts to the NIH National Library of Medicine PubMed database.
• IPD Sharing Time Frame: The data will be made available to other users within 12 months of the final data point being collected or at the time of an associated publication, whichever comes first.

IPD Sharing Access Criteria

All research participants will be consented for broad data sharing. To protect the privacy and confidentiality of the participants, raw video or audio files will not be shared outside of the research team. We do not commit to processing additional data beyond our own needs for the purpose of sharing. Any data we have processed we are willing to share.

To request access of the data, researchers will use the standard processes at NDA, and the NDA Data Access Committee will decide which requests to grant. The standard NDA data access process allows access for one year and is renewable.

The NDA GUID tool allows researchers to aggregate data from the same research participant without different laboratories having to share personally identifiable information about that research participant. The NDA data dictionaries do not permit personally identifiable information to be shared. NDA maintains a Certificate of Confidentiality.

• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No