DOI: 10.5061/dryad.g79cnp5x6
title: Data for: Will you read how I will read? Naturalistic fMRI predictors of emergent reading

description: 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.
description: 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.
description: # Data for: Will you read how I will read? Naturalistic fMRI predictors of
 emergent reading
 [https://doi.org/10.5061/dryad.g79cnp5x6](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](https://doi.org/10.1016/j.neuropsychologia.2023.108763 "Persistent link using digital object identifier") 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](https://github.com/rordenlab/dcm2niix). Structural data bear the naming prefix "anat" and have been skull-stripped by [AFNI's sswarper2](https://afni.nimh.nih.gov/pub/dist/doc/program_help/sswarper2.html). 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)](https://github.com/pmolfese/Wat2023) for potential updates. ## Code/Software Code for processing the data is shared on GitHub: [https://github.com/pmolfese/Wat2023](https://github.com/pmolfese/Wat2023) Data are primarily processed in [AFNI](https://afni.nimh.nih.gov/). This includes preprocessing of the structural and functional MRI data as well as the inter-subject correlation (ISC) processing using [3dISC](https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dISC.html). 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.](https://naturalistic-data.org/content/Intersubject_RSA.html).