Data for: Will you read how I will read? Naturalistic fMRI predictors of emergent reading
Data files
Dec 29, 2024 version files 2.05 GB
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README.md
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ta0084.tgz
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wat2023_behavioral.csv
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Abstract
Despite reading being an essential and almost universal skill in the developed world, reading proficiency varies substantially from person to person. To study why, the fMRI field is beginning to turn from single-word or nonword reading tasks to naturalistic stimuli like connected text and listening to stories. To study reading development in children just beginning to read, listening to stories is an appropriate paradigm because speech perception and phonological processing are important for, and are predictors of, reading proficiency. Our study examined the relationship between behavioral reading-related skills and the neural response to listening to stories in the fMRI environment. Functional MRIs were gathered in a 3T TIM-Trio scanner. During the fMRI scan, children aged approximately 7 years listened to professionally narrated common short stories and answered comprehension questions following the narration. Analyses of the data used inter-subject correlation (ISC), and representational similarity analysis (RSA). Our primary finding is that ISC reveals areas of increased synchrony in both high- and low-performing participants previously implicated in reading ability/disability. Of particular interest are that several previously identified brain regions (medial temporal gyrus (MTG), inferior frontal gyrus (IFG), inferior temporal gyrus (ITG)) were found to “synchronize” across higher reading ability participants, while poor performers showed desynchronization from both proficient readers and other inefficient readers. Additionally, two regions (superior frontal gyrus (SFG) and another portion of ITG) were recruited by all participants, but their specific time course of activation depended on reading performance. These analyses support the idea that different brain regions involved in reading follow different developmental trajectories that correlate with reading proficiency on a spectrum rather than the usual dichotomy of poor readers versus strong readers.
README: Data for: Will you read how I will read? Naturalistic fMRI predictors of emergent reading
https://doi.org/10.5061/dryad.g79cnp5x6
These data are from the Neuropsychologia publication "Will you read how I read? Naturalistic fMRI predictors of emergent reading":
Wat, E. K., Jangraw, D. C., Finn, E. S., Bandettini, P. A., Preston, J. L., Landi, N., Hoeft, F., Frost, S. J., Lau, A., Chen, G., Pugh, K. R., & Molfese, P. J. (2023). Will you read how I read? Naturalistic fMRI predictors of early reading. Neuropsycholgia, 108763. https://doi.org/10.1016/j.neuropsychologia.2023.108763
The dataset represents data from 34 participants collected in a 3T MRI environment. Both structural (anatomical) and functional (EPI) data are available in this archive. Data are "Raw" NIFTI files converted directly from DICOM using dcm2niix. Structural data bear the naming prefix "anat" and have been skull-stripped by AFNI's sswarper2. Functional data begin with the prefix "ep2dbold".
Description of the data and file structure
All data files are organized by participant. Participant folders are represented by an anonymized scanning ID (e.g. "ta1234") which represents the sequential incrementation of all data collected on a particular MRI during the time of data acquisition. Within this folder are subfolders for structural/anatomical data (i.e., anat) and functional data (i.e., func_story).
Within the anatomical folder is a single anatomical file named anatomical anat.nii.gz
which is the full skull-stripped anatomical image that was passed to afni_proc.py for processing.
Within the func_story folder, there are four "runs" or files of EPI functional images. These are ordered by the run number and should be handled in the sequential order they are labeled. In the manuscript the first 6 TRs were removed to reach steady-state before processing, these images remain in the data here and should be trimmed if you wish to replicate our process.
In addition to the MRI data, there is a behavioral data file titled "wat2023_behavioral.csv" which includes the individual reading scores, and MRI ID. Columns in this file include WJWA_SS (Woodcock-Johnson Word Attack), WJLWID_SS (Woodcock-Johnson LetterWord ID), SWE_SS (TOWRE Sight Word Efficiency), and PDE_SS (TOWRE Phonetic Decoding Efficiency). All scores are standardized by the neuropsychological measure to the age of the participant.
Sharing/Access information
Data is shared here on Data Dryad. Other sources may be added in the future and this section will be updated accordingly. You may check the main GitHub repository for this paper (https://github.com/pmolfese/Wat2023) for potential updates.
Code/Software
Code for processing the data is shared on GitHub: https://github.com/pmolfese/Wat2023
Data are primarily processed in AFNI. This includes preprocessing of the structural and functional MRI data as well as the inter-subject correlation (ISC) processing using 3dISC. Linking fMRI data to behavioral data was conducted primarily in Python (and verified in R). Instructions and notebooks for performing RSA analysis on fMRI data can be found at naturalistic-data.org..
Methods
Functional MRI data were collected using a Siemens TIM-Trio 3 T MRI system, with a 12-channel head coil. T1-weighted MPRAGE structural images were acquired with the following parameters: matrix size = 256 × 256; voxel size = 1 × 1 × 1 mm; FoV = 256 mm; TR = 2530 ms; TE = 3.66 ms; flip angle = 7°. These were followed by gradient echo echo-planar image (EPI) functional scans with the following parameters: 32 slices; 4 mm slice thickness; no gap, matrix size = 64 × 64; voxel size = 3.4375 × 3.4375 × 4 mm; FoV = 220 mm; TR = 2000 ms; TE = 30 ms; flip angle = 80°.
Data here are raw (unprocessed) data, converted to NIFTI, and anonymized by removing meta-data, PII, and skull-stripping anatomical images. While these data are not in BIDS format, they are in folders reminiscent of BIDS.