Reading Development project

The Reading Development project uses an open dataset collected between 2008-2013 in James Booth's Developmental Child Neuroscience Lab at Northwestern. The data are available at openneuro.org.

General Notes

To be added later.

Constrained Classifier Project

Participants

28 Children (14 TD, 14 RD) with either strong or weak (incl. dyslexia diagnosis) reading skills and no other diagnosis and data from 2 time points. Children were scored according to Z score on 3 reading measures (WATT, Pseudoword decoding, something else), with an additional -1 or -0.5 assigned for a clinical dyslexia diagnosis (-1) or reading difficulty diagnosis (-.5). I selected the children with the top and bottom 14 summed scores.

Preprocessing

Anatomical surfaces reconstructed using T1w images at both early and late time points. Functional data from VV runs (word and nonword) preprocessed with both 6mm and 2mm blurring, Siemens slice time.

Functional Masking Using GLMA

A GLMA was carried out, contrasting all lexical vs. baseline to create group-level functional masks for TD and RD (p<.001, cluster P<.05). Union of masks was calculated. Masks were subdivided along template anatomical boundaries using custom scripts intersection clusters with template region boundaries. These regions were further subdivided using mris_divide_whatever.

Time Series Extraction

The 2mm-smoothed data were detrended using detrend_wmcsf.sh to remove linear trend, motion and wm and csf signal as nuisance regressors. The surfacetimecourses.sh shell script was used to extract time series in fsaverage space to plaintext files.

Functional Connectivity Estimation

Cross-mutual information (XMI) was used to calculate a functional connectivity matrix for each functional run (8 runs/participant) using a custom MATLAB script.

MATLAB FC Pseudocode

for each subject S:
   for each time series file:
      load the files for lh and rh
      merge lh and rh into a single ts matrix
      eliminate timepoints at spikes (3SD from mean)
      initialize empty adjacency matrix A
      for each ordered pair of regions (i,j) in upper triangle of A:
         calculate the number of bins required for the cross-mutual-information calculation
         calculate the cross-mutual-information for time series in regions i and j and store in A[i,j]
    append A to collection of adjacency matrices for S
 save all adjacency matrices for S to a .mat file

FC Pattern Generation

Upper triangle of XMI matrix was extracted as a vector and normalized (Z Score) and scaled to fall between 0 and 1. The resulting distribution was very positively skewed so the square root of these values was calculated to make the distribution more normal. The scaled FC vectors for each of the 8 runs per participant were tagged for lexicality (word=1, pseudoword=0) at element n-1, and reading skill (RD=0, TD=1) at element n. This generated a matrix of 224 (28 subjects * 8 runs) by 6788 (6786 connections + lexicality code + group code). The matrix was written to a single .csv file.