Poster No:
816
Submission Type:
Abstract Submission
Authors:
Iria Gutierrez-Schieferl1, Alison Schug2, Guinevere Eden2
Institutions:
1Georgetown University, Gloucester, MA, 2Georgetown University, Washington, DC
First Author:
Co-Author(s):
Introduction:
Relatively less gray matter volume (GMV) has been reported in Reading Disability (RD, or developmental dyslexia) in left-hemisphere occipitotemporal, orbitofrontal/inferior frontal, bilateral temporoparietal regions and cerebellum (Eckert et al., 2016; Linkersdörfer et al., 2012; Richlan et al., 2013), aligning with the theory of a language-based learning disability. However, cross-sectional neuroanatomical studies cannot disambiguate differences causal to RD from reduced experience-dependent changes brought about by learning to read (Carreiras et al., 2009, Krafnick et al., 2014), and which are consequential to RD (Clark et al., 2014; Kuhl et al., 2020). Similarly, concurrent correlations between GMV and reading performance also cannot determine causality. Here we address this issue using longitudinal data to not only test for GMV differences that manifest in RD across two time points, but also those that manifest as a function of time. Importantly, we tested if regions that differ in GMV predict future reading performance (2 years later), indicating a causal role.
Methods:
This study utilized Baseline and 2-Year Follow-Up data from the Adolescent Brain & Cognitive Development (ABCD) Study. Participants were native-English, monolingual speakers with a Matrix Reasoning score >85, a NIH Toolbox Picture Vocabulary Test score >70, no history of neurological or psychological disorders, and no sensory or visual impairments. Reading Disability (RD) was defined as a standard score <85 (16th percentile) on the Toolbox Oral Reading Recognition Test (TORRT) at both Baseline and Follow-Up, while Controls scored >90 at these time points. Propensity matching ensured comparable socioeconomic status (SES, household income), and Matrix Reasoning scores for the RD (N=217) and Control groups (N=214) at Baseline (mean age 9.53 ± 0.5 years) and Follow-Up (mean age 11.47 ± 0.6 years). Structural magnetic resonance imaging (MRI) data underwent standard voxel-based morphometry preprocessing using SPM12. A 2x2 ANOVA was conducted to examine the main effects of Diagnostic Group (RD vs. Control), Time Point (Baseline vs. Follow-Up), and their interaction. Covariates included total GMV, SES, study site, ADHD diagnosis, sex, and pubertal status. Statistical thresholds were set at p < .005 (voxel-wise, uncorrected) and p < .05 (cluster-level, FDR-corrected). For regions that differed in GMV (RD vs. Control), we created a mask to test for voxel-wise correlations in these regions at the first Time Point with reading performance at the second Time Point.
Results:
The main effect of Diagnostic Group revealed relatively less GMV in the RD group across both Time Points in left pallidum, middle temporal pole, paracentral lobule, inferior temporal gyrus (extending into fusiform gyrus), as well as right anterior cingulate cortex, precuneus, temporal pole, and inferior/middle temporal gyrus. Conversely, the RD Group had greater GMV in numerous bilateral frontal, parietal, and temporal regions. There were no interactions between Group and Time Point. In regions where GMV differed (across both Time Points), GMV in left pallidum at the first Time Point positively predicted reading performance at the second Time Point.
Conclusions:
Less GMV in children with RD were primarily identified in left and right temporal lobe regions and left basal ganglia (across both Time Points) and was dwarfed by the more numerous and far-reaching regions of greater GMV in RD. This indicates more aberrations in RD in this study than reported in the prior literature using smaller groups. The lack of an interaction result indicates no effect of reading experience on GMV differences in RD over two years. Only subcortical GMV predicted stronger reading two years later, suggesting a causal role of the left basal ganglia in RD (Ullman et al., 2020). Overall, these results represent a departure from the traditional theory of the brain bases of dyslexia.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Language:
Reading and Writing 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Neuroinformatics and Data Sharing:
Databasing and Data Sharing
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Basal Ganglia
Cognition
Development
DISORDERS
Language
Learning
MRI
STRUCTURAL MRI
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Not applicable
Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Provide references using APA citation style.
Carreiras M, Seghier ML, Baquero S, et al. An anatomical signature for literacy. Nature.
2009;461(7266):983-986.
Clark KA, Helland T, Specht K, et al. Neuroanatomical precursors of dyslexia identified
from pre-reading through to age 11. Brain J Neurol. 2014;137(Pt 12):3136-3141.
Eckert MA, Berninger VW, Vaden KI, Gebregziabher M, Tsu L. Gray Matter Features of
Reading Disability: A Combined Meta-Analytic and Direct Analysis Approach(1,2,3,4). eNeuro. 2016;3(1).
Krafnick AJ, Flowers DL, Luetje MM, Napoliello EM, Eden GF. An investigation into the
origin of anatomical differences in dyslexia. J Neurosci. 2014;34(3):901-908.
Kuhl U, Neef NE, Kraft I, et al. The emergence of dyslexia in the developing brain.
NeuroImage. 2020;211:116633.
Linkersdörfer J, Lonnemann J, Lindberg S, Hasselhorn M, Fiebach CJ. Grey Matter
Alterations Co-Localize with Functional Abnormalities in Developmental Dyslexia:
An ALE Meta-Analysis. PloS One. 2012;7(8):e43122.
Richlan F, Kronbichler M, Wimmer H. Meta-analyzing brain dysfunctions in dyslexic
children and adults. NeuroImage. 2011;56(3):1735-1742.
Ullman MT, Earle FS, Walenski M, Janacsek K. The Neurocognition of Developmental
Disorders of Language. Annu Rev Psychol. 2020;71:389-417.
No