Neural Mechanisms of Successful Intervention in Children With Dyslexia
Phase: NA
Conditions: Dyslexia, Developmental
Sponsor: Stanford University

 
 
 
 
Neural Mechanisms of Successful Intervention in 
Children with Dyslexia 
 
 
Study Protocol and Statistical Analysis Plan 
NCT04323488 
October 29, 2018 
 
 
 

 SPECIFIC AIMS - Neural mechanisms of successful intervention in children with dyslexia 
Reading instruction prompts the emergence of neural circuits that are specialized for rapidly translating printed 
symbols into sound and meaning. Understanding how these circuits differ in children with dyslexia, and change 
with learning, is an important scientific challenge that holds practical implications for education. The proposed 
research employs frequent longitudinal measurements over an intensive summer intervention program for 
children with dyslexia to: (a) determine how brain structure and function change in response to reading 
instruction; and (b) investigate neurobiological factors that predispose a child to struggle or succeed in the 
intervention. Thus, this proposal seeks to determine both how education shapes brain development, and how a 
child’s unique neurobiology predicts educational outcomes.  
Aim 1) White matter plasticity and learning - White matter was previously considered static infrastructure; it 
is now known that the thickness of the myelin sheath, the caliber of axons, and density of glial cells change with 
learning. Recent studies in mouse models demonstrate the central role of white matter plasticity in the learning 
process, and highlight the importance of understanding white matter plasticity in humans1–3. Reading 
interventions provide a powerful tool to study experience-dependent plasticity in the human brain. Based on 
novel quantitative MRI and diffusion MRI techniques developed by the PI and collaborators4–8, it is now possible 
to quantify changes in cell density, intra-axonal water and myelination at millimeter resolution. 
Approach: Forty children with dyslexia (ages 9-11y), will be recruited for a highly effective intervention program 
that involves intensive (160 hours over 8 weeks) one-on-one training in phonological and orthographic 
processing skills9–11. Based on dense longitudinal measurements collected before, during, and after the 
intervention, we will model the time-course of white matter plasticity associated with improvements in reading 
skills and investigate the biological mechanisms that underlie differences in learning among children. Aim 1 
targets three questions: 1a: Does intervention cause short-term, transient changes in the white matter, or long-
term remodeling of the brain circuit? 1b: Do different cellular properties of the white matter display different time-
courses of plasticity? 1c: Is white matter plasticity coupled to behavioral improvement? 
Aim 2) Bottom-up and top-down computations in the reading circuit - When our eyes fixate upon a word, a 
cascade of neural processes is initiated, beginning in the visual system and progressing through a series of 
computations that translate the visual representation into sound and meaning. The visual word form area 
(VWFA)12–15 is the intersection of vision and language, and has direct connections to visual cortex and language 
areas. Based on meta-analysis16, the VWFA is the most common location of neural deficits in dyslexia. We have 
developed the first model of neural computations performed by the VWFA, characterizing both: (a) the automatic, 
or bottom-up, response of neurons that encode words, and (b) the effect of top-down signals that modulate the 
VWFA response17. Based on this model, we will investigate how computations and connectivity of the VWFA 
differ in children with dyslexia, and change in response to reading instruction. 
Approach: FMRI experiments measuring stimulus-driven (bottom-up) neural tuning properties and top-down, 
task-dependent modulation of the VWFA (adapted from ref17) will be collected longitudinally over the course of 
the intervention program. By measuring changes in neural computations over a period of targeted training, while 
tightly controlling both the stimulus properties and cognitive task demands, we will address three broad 
questions: 2a: To what extent do VWFA deficits, and learning-induced changes, reflect bottom-up neural tuning 
properties versus functional connectivity between visual and phonological processing regions? 2b: Do VWFA 
changes occur in concert with changes in phonological processing regions? 2c: Do changes in white matter 
connectivity increase the efficiency of top-down signals and improve functional connectivity between visual and 
phonological processing regions? 
Aim 3) Neural biomarkers of learning outcomes - Even in a controlled and intensive learning environment, 
some children show substantial (>2 SD) improvements in reading skill, while others show limited change. What 
biological factors predispose a child to excel or struggle when provided a high-quality intervention? Data from 
multiple labs show converging evidence that tissue properties of the arcuate fasciculus predict the likelihood that 
a child will struggle learning to read. However, one of the limitations of past research is the lack of control over 
educational experience, a problem that is addressed by the summer intervention program employed here.  
Approach: Pre-intervention MRI measurements will be examined as predictor variables (in combination with 
behavioral measures) for individual differences in intervention learning rate, and long-term, post-intervention 
outcomes. Previous research and pilot data support 3 hypotheses: 3a: Poor integrity of the arcuate fasciculus 
will predict resistance to remediation above and beyond behavioral measures. 3b: The severity of neural 
processing deficits in components of the reading circuitry will predict learning outcomes. 3c: Combining 
behavioral measures and data from the different MRI modalities with machine learning will lead to more accurate 
predictions than any single modality on its own.  

RESEARCH STRATEGY - Neural mechanisms of successful intervention in children with dyslexia 
Significance 
Overview: The proposed experiments combine a highly effective dyslexia intervention program9,10 with a 
sequence of cutting-edge neuroimaging measurements to study the neurobiology of learning in the context of a 
controlled and intensive educational program. The methodology builds from measurements of brain circuit 
structure (Aim 1), to neural computation (Aim 2), and then combines the data from the first two aims to examine 
the predictive value of brain measurements as biomarkers for future learning (Aim 3). Integrating these three 
measurement modalities – behavior, structure and function – will motivate a new circuit-level understanding of 
the neurobiological basis of learning to read, and the underpinnings of learning difficulties in dyslexia.  
Promoting the virtuous cycle between neuroscience and education: Interdisciplinary research at the 
intersection of education and neuroscience promises to catalyze discovery in both fields. On the one hand, 
neuroimaging is a powerful tool for understanding the mechanisms that underlie the successful treatment of 
learning disabilities such as dyslexia. A deeper understanding of the neurobiological underpinnings of individual 
differences in learning will pave the way for innovative treatment programs that are personalized to a child’s 
unique pattern of brain maturation. On the other hand, intervention programs offer a powerful tool for studying 
basic mechanisms of experience-dependent plasticity in the human brain and making causal inferences about 
how changes to the environment prompt reorganization of brain circuits. Much of what we know about plasticity 
comes from research in model organisms that show dramatically different developmental time-courses from 
humans. By following children longitudinally through targeted education programs, we can answer fundamental 
questions about how experience shapes brain development and forge new links between neuroscience theory 
and education practice. 
Understanding individual differences in reading outcomes: Learning to read is at the foundation of academic 
success. Formal reading instruction begins in kindergarten and by fourth grade every child is expected to fluidly 
decode written text into sound and meaning. While the focus of the first few years of elementary school is learning 
to read, children are quickly expected to use reading as a tool for learning across all academic domains: math is 
taught through word problems, history through textbooks, and reasoning skills through analyzing texts. Children 
who struggle learning to read quickly find themselves struggling across academic domains18.  
Fortunately, scientific research on the mechanisms underlying dyslexia has led to the development of effective 
intervention programs to improve readings skills in children with dyslexia19–29. Even though interventions are 
successful at the group level, there is always a subset of  “treatment resisters” who do not show substantial 
improvements in reading scores30. On the other end of the spectrum, some children show rapid and 
transformative improvement in reading skill during an intervention. Behavioral measures have been established 
as useful predictors of intervention outcomes (for review see31,32), but even after accounting for baseline 
behavioral differences, there is still substantial unexplained variance. This fact has prompted great interest in 
exploring brain imaging measurements as potential biomarkers of children’s future learning trajectories33–43. 
Here, we explore the hypothesis that an individual’s response to intervention can be accurately predicted based 
on properties of their reading circuitry, and that treatment resisters can be identified based on specific deficits 
that impede the learning process. By collecting pre-intervention measurements of white matter connectivity (Aim 
1), and neural response properties (Aim 2), we will endeavor to build a model of the subject-specific 
characteristics that predict success in the intervention program (Aim 3). Our approach will leverage the extensive 
behavioral literature31,32,44 and test the hypothesis that specific neural biomarkers will improve prediction 
accuracy. Such a model holds practical value given that the costs of an intensive intervention program far 
exceeds that of an MRI scan. Moreover, by testing specific biomarkers from the literature in the context of an 
intervention study, we are uniquely poised to determine the factors that predict individual differences in learning, 
when children are in a controlled, and high-quality educational setting.  
The role of white matter in reading development: Seminal work by Klingberg and colleagues first 
demonstrated the relationship between white matter tissue properties and reading skill45. Subsequently, dozens 
of studies have examined correlations between diffusion MRI measurements of the white matter and reading 
skill33,35–38,42,46–56. Based on this extensive literature, there are two fundamental, unanswered questions. First, do 
anatomy-behavior correlations reflect static traits that differentiate individuals, or do correlations arise due to 
differences in educational experience/environment? Our recent work suggests that the white matter is 
surprisingly plastic (see Aim 1 pilot data), opening the possibility that anatomy-behavior correlations emerge as 
temporary states in a highly dynamic system11. And yet, baseline diffusion measurements also predict 
intervention outcomes (Aim 3 pilot data). The proposed research will clarify the role of educational experience 

in shaping white matter tissue properties. Second, what is the underlying biology relating diffusion MRI measures 
to behavior? Diffusion measurements are incredibly sensitive to individual differences in tissue structure, but are 
not specific to any one biological property35,57–60. Based on our innovative quantitative MRI measurement protocol 
(Aim 1), we will clarify the biological source of white matter-behavior correlations, and group differences in 
dyslexia. This will have a significant impact on the reading literature, and more broadly in cognitive neuroscience 
for interpreting correlations between diffusion properties and behavior and establishing closer links to research 
in animal models. 
Open science: The PI has made a strong commitment to open science through the development and support 
of one of the most widely used, open-source software packages for quantitative analysis of white matter tissue 
properties6, and the release of analysis code and data to reproduce findings from published work in his lab4,9–
11,17,61. All data from this grant will be made publicly available in order to facilitate scientific reproducibility, and to 
allow other researchers to tackle new and innovative questions that were not conceptualized in the original 
proposal62. This will represent one of the most innovative and significant public datasets targeting plasticity and 
learning and will be valuable to a broad collection of researchers. 
Innovation 
Understanding how the developing brain builds circuits to rapidly translate printed symbols into meaning is an 
important scientific challenge with practical implications for education. This proposal capitalizes on a cutting edge 
suite of measurement techniques and software algorithms that the research team has developed to model the 
biological processes that underlie learning to read4–7,63. Specific innovations in our methodology are highlighted 
alongside pilot data for each aim. Training the brain to decode text is a powerful paradigm to investigate 
mechanisms of plasticity. By employing an intervention that is delivered in an intensive (20 hrs/week) one-
on-one setting, we have the opportunity to investigate large-scale plasticity in the white matter that has 
not been observed in less intensive intervention paradigms (see pilot data Aim 1 and11). These 
measurements will answer fundamental questions about the nature of experience-dependent plasticity in the 
human brain and generate discoveries that can inform clinical and educational practice. Unlike previous reading 
intervention research, the primary focus of this proposal is to link learning mechanisms with the broader 
neuroscience literature; through close collaboration and data sharing with Ken Pugh (Haskins) and Richard 
Wagner (Florida State) we will also relate these data to cognitive models of the reading architecture.  
Approach 
Overview: The goal of this proposal is not to determine the efficacy of any specific 
intervention program but, rather, to capitalize on an intervention with proven 
efficacy as a tool to study the biological underpinnings of learning. Forty children 
with dyslexia will be recruited to participate the Lindamood-Bell Seeing Stars 
intervention, which combines training in phonological and orthographic processing 
(details can be found in our recent work9,10 and the published manual64). Pilot data 
on 26 children with dyslexia demonstrate highly significant (p<10-10) changes in 
reading skills as indexed by the Woodcock Johnson Basic Reading Skills 
composite, with average improvements of 0.9 SD over 8 weeks (Figure 1). 
Reading automaticity (Test of Word Reading Efficiency (TOWRE) p<10-6) and 
Reading Fluency (p<10-5) also show highly significant change, confirming the 
generalizability of the intervention.  
Each child will participate in six measurement sessions. Two sessions will occur 
prior to the intervention and will serve as a control period (individual baseline); one 
session will be conducted at the mid-point of the intervention; one immediately 
post-intervention; two long-term follow up sessions will be conducted at 6- and 12-
months post-intervention. Multiple baseline sessions will allow each child to serve 
as their own control, and intervention-driven change will be compared to changes observed during the control 
period65. For the aims of this proposal, multiple baseline sessions are preferable to a wait-list control group 
because it makes it possible to control for developmental effects within the same subjects that participate in the 
intervention. For example, given the focus on individual differences (Aim 3), it is important to consider each 
subject’s baseline (pre-intervention) growth rate as a potential covariate for differences in growth during the 
intervention period. Additionally, two control groups (see below) will serve as a comparison for long-term 
intervention effects. 
Figure 1: Reading skills 
improve substantially and 
systematically 
over 
the 
course of the Seeing Stars 
intervention and are stable 
during the baseline period. 

Behavioral assessment: The Woodcock Johnson IV (WJ, Basic Reading Skills and Reading Fluency), 
TOWRE-2, Gray Oral Reading Tests (GORT-5, Rate, Accuracy, Fluency, Comprehension), and the Test of Silent 
Reading Efficiency and Comprehension (TOSREC) will be used to assess reading automaticity, accuracy and 
comprehension at each session. Phonological awareness (PA), phonological memory (PM) and rapid naming 
(RN) will be assessed with the Comprehensive Test of Phonological Processing (CTOPP). An auditory phoneme 
categorization task will be used to gain additional insights into phonological processing66. Additionally, parent 
questionaires will be used to assess (1) home literacy environment, education history, and current education 
experience67, and (2) ADHD symptoms using the Strengths and Weaknesses of ADHD-Symptoms and Normal-
Behavior (SWAN) rating scale68,69. 
Participants and experimental design: Subjects will be recruited from the University of Washington Reading 
and Dyslexia Research Database, a subject pool run by the PI. On average, 125 subjects per year are recruited 
into the Database, and each subject receives an extensive battery of behavioral tests (including the WJ, TOWRE, 
CTOPP, and Wechsler Abbreviated Scales on Intelligence (WASI)), MRI screening, and practice at an MRI 
mock-scanner that includes training on holding still. Additionally, subjects fill out questionnaires on education 
history, home environment, socioeconomic status (SES) and ADHD symptoms. Although the aims of this 
proposal do not target the relationship between SES and response to intervention, obtaining this information will 
allow other researchers to explore this relationship when data are made public (e.g.70). This well-characterized 
subject pool makes it makes it possible to recruit matched samples of intervention and control subjects with 
specific behavioral and demographic characteristics. In our experience, there is very low subject attrition because 
all subjects have already visited the lab and are familiar with the research protocol. 
Intervention subjects (N=40) will be children between nine and eleven years of age who are below the 25th 
percentile in timed (TOWRE composite) and untimed (WJ Basic Reading Skills) single word decoding abilities, 
and within the normal range (+/- 1 SD) on measures of general cognitive abilities (WASI). Diagnosis of ADHD 
will be an exclusion criterion due to concerns over motion. Pilot measurements on children 7-12 years of age 
demonstrate that the intervention is equally effective (d=~.9) across this age range9. By focusing on 9-11-year-
old children less data will be lost due to motion and subjects will stay focused for longer scan sessions.  
Intervention protocol: Intervention will be administered to participants in Lindamood-Bell learning centers in 
the Seattle area four hours a day, five days a week, for eight consecutive weeks during summer vacation. Trained 
tutors will work one-on-one with each child. Importantly, the scientific research will be completely independent 
of the administration of the intervention to eliminate any potential conflicts of interest. Lindamood-Bell will not 
have access to any information the PI’s laboratory collects on the subjects, including subject attrition.  
The Lindamood-Bell “Seeing Stars” instruction program teaches phonological and orthographic processing 
through a combination of mental imagery and sensory-motor learning. Children practice visualizing the 
orthography of words, starting from simple consonant-vowel syllables and systematically working into more 
complex consonant-vowel-consonant pairings. The instruction focuses on segmenting and blending strings of 
phonemes into words and visualizing the relationship between the articulatory elements, and their representation 
as visual symbols. Phonological awareness is systematically built through articulatory exercises where children 
are taught to attend to the relationship between motor movements of the mouth and tongue, and speech sounds 
in words, and the corresponding letters. As children build a stronger foundation of phonological and orthographic 
knowledge, additional practice with increasingly complex sight-word identification and phonological decoding is 
layered into the intervention.  
Intervention fidelity and monitoring: The intervention curriculum is detailed in the publicly available Seeing 
Stars Teacher’s Manual allowing the procedure to be precisely reproduced64. Fidelity to the published curriculum 
will be ensured in two ways. First, Lindamood-Bell has established a rigorous methodology for ensuring the 
quality and fidelity of instruction: (1) Each instructor completes a 3-week training program; (2) At each learning 
center a “coach” with multiple years of experience monitors each instructor’s progress and provides feedback, 
ensuring that the instructor is properly progressing through the steps of the curriculum with their student; and (3) 
Each coach provides weekly reports to the regional director to ensure consistency of implementation across 
learning centers. This fidelity monitoring will be recorded and databased for each research study participant. 
Second, one session a week (for each child) will be recorded with a digital video camera and a member of the 
research team (independent of Lindamood-Bell) will score the fidelity of implementation for each session based 
on: (1) adherence to the procedures of the intervention using a 5-point implementation behavior scale and (2) 
engagement of the child. Child engagement will be measured using a 5-second partial interval time sampling 
procedure on the same videotaped session used for procedural fidelity. Reliability procedures will require 
research staff to rate a sample of training videos and for their ratings to correlate at r ≥ .80 prior to data collection. 

The researcher will provide regular feedback to the Lindamood-Bell regional director based on the independent 
assessment of fidelity so that instructors can receive coaching in the case of deviations from the published 
curriculum. Finally, scoring will be databased so that details of the intervention delivery can be examined as 
covariates in statistical models of intervention effects. 
Control subjects: Multiple baseline measurements in each intervention subject will be used to establish the 
stability of our measurements, and control for the effects of repeated testing over the intervention period. 
Additionally, two control groups will be recruited to assess long-term change in the absence of intervention. 
Each control group will include 20 subjects matched in terms of age, gender, non-verbal IQ and 
socioeconomic status. One control group will also be matched in terms of reading skills (dyslexic control) 
and will serve as a comparison for post-intervention changes in children with dyslexia. The other control group 
will consist of typical readers and will serve as a comparison for typical development. Based on the typical-
reading control group, we can assess neural differences in the subjects with dyslexia and examine 
“normalization” of deficits associated with dyslexia. Control groups will participate in four measurements 
sessions: (1) a baseline session followed by longitudinal visits at (2) two months (length of intervention), (3) eight 
months and (4) 14 months. Dyslexic control subjects will be offered the opportunity to participate as intervention 
subjects the following summer. 
Statistics: Longitudinal change will be modeled with linear mixed effects (LME) models. LME models are 
preferable to repeated measures ANOVAs for two reasons: (1) subjects with missing data points do not need to 
be excluded; (2) non-linear effects can be modeled by adding higher-order polynomials terms. Model selection 
based on Bayesian information criteria will be used to determine the random effects structure71–73. 
Aim 1: White matter plasticity and mechanisms of learning 
Significance: Interventions are a powerful tool to examine how an experimental manipulation of a child’s 
environment impacts brain development. Aim 1 seeks to determine how remediation of reading difficulties 
changes the wiring of the human brain, and to resolve the long-term time-course of plasticity and learning that is 
set in place by an intensive summer intervention11. Aim 1 will answer basic scientific questions of how experience 
shapes brain development and generate a new understanding of the mechanisms underlying dyslexia 
remediation. Investigating how experience changes the white matter will provide critical data to interpret the 
hundreds of studies demonstrating correlations between white matter tissue properties and behavioral 
measures, and the dozens of studies showing correlations specific to reading skills (for reviews see35,74–79). 
Innovation: Over the past decade, non-invasive techniques to measure the living human brain have dramatically 
improved. Research on experience-dependent plasticity that was once only feasible in animal models can now 
be conducted in living and behaving human subjects. Properties of cellular organization including the density of 
tissue macromolecules and the concentration of myelin can be accurately estimated using recently developed 
quantitative MRI techniques employed here4,5. Now, for the first time, we can begin to develop models of the 
biological processes that underlie the development of uniquely human skills such as reading. Here we harness 
new innovations in diffusion MRI and quantitative MRI acquisitions, combine these measures with biophysical 
modeling, and collect measurements longitudinally over a tightly controlled educational intervention, to pioneer 
a new understanding of the neurobiology of learning. The PI’s laboratory is uniquely poised to implement these 
cutting-edge measurement techniques in an intervention study. 
Relation to previous work: Keller and Just (2009) were the first to report intervention-driven changes in the 
white matter, establishing the feasibility of measuring white matter plasticity during a reading intervention80. But 
a number of questions were left unanswered by this original work: First, the behavioral effects were relatively 
small (significant improvement in pseudo-word but not real-word reading or comprehension), and the white 
matter changes were localized to a small region and not in any of the core reading tracts. Therefore, white matter 
plasticity is likely to be much more extensive during an intensive intervention program (see Figure 2 and ref11). 
Also, the intervention was carried out over an extended period of time (six months during regular schooling), 
which introduces maturational and environmental confounds. Third, quantitative MRI methods capable of 
resolving different biological mechanisms in the white matter were not available at the time. Finally, long-term 
follow-ups were not conducted to ascertain the stability of learning effects. Thus, employing an intensive 
intervention over 8 weeks of summer, combined with innovative, biologically specific imaging methods and long-
term follow-up measures and questionnaires will allow us to address significant, unanswered questions. 

Quantitative MRI measurements of white matter tissue structure 
Diffusion weighted MRI (dMRI) data acquisition – DMRI data will be acquired at 1.5 mm3 spatial resolution 
(96 gradient directions, distributed across 2 b-values, b=711 and b=2855 s/mm2). 64 diffusion directions will be 
sampled at the high b-value, 32 directions at the low b-value, and 8 images will be acquired without diffusion 
weighting (b=0). Half the b=0 images will be acquired with a reversed phase encoding direction to correct 
distortions due to field inhomogeneities81,82. This sequence was determined optimal for estimation of parameters 
of the neurite orientation dispersion and density imaging (NODDI) model, based on data from a Philips Achieva 
scanner83. The acquisition optimally balances (a) angular resolution/contrast to estimate fiber orientation 
distribution functions for tractography84, (b) multiple shells to estimate tissue properties based on multiple 
compartment models83 and (c) signal to noise ratio (SNR) given the time constraints of working with children. 
Fiber tractography – Fiber orientation distribution functions will be estimated using constrained spherical 
deconvolution (CSD85) as implemented in the Diffusion in Python (DIPY) software package86. Optimal 
parameters for model fitting will be determined based on cross-validation8. Fibers will be estimated using 
probabilistic tractography. Given concerns over false positives in tractography (erroneous fibers) highlighted by 
us and others87, the Linear Fascicle Evaluation (LiFE) algorithm will be used to control for false positives, and 
retain an optimized set of fibers7. Fascicles will then be identified within each individual’s brain using the AFQ 
software package6, and analyzed in relation to reading skills over the course of the intervention. AFQ 
measurements are highly reliable (r = 0.936) and are, therefore, well suited for longitudinal studies.  
Modeling tissue structure from dMRI measurements – The diffusion of water molecules probes the 
microscopic structure of brain tissue. Membranes create obstacles to the diffusion process, leading to declines 
in the rate of diffusion, and creating separate compartments of intra- and extra-cellular water that can be 
distinguished with dMRI. The multi-b-value, dMRI sequence of the proposed study will make it possible to 
estimate the proportion of water that is restricted within cell bodies and axons (neurites83), adding an additional 
layer of specificity to our biological interpretation. Mean diffusivity (MD) and fractional anisotropy (FA) will be 
computed to characterize Gaussian diffusion and intracellular water volume fraction (based on the NODDI 
model83) will be calculated to characterize changes in the amount of water that is contained within cellular 
structures. Intracellular volume fraction will change if axons grow larger in diameter in during learning.  
T1 and MTV mapping – Our goal is to go beyond simply localizing learning-induced changes and work towards 
understanding the neurobiology of these changes. Diffusion measurements will be combined with quantitative 
MRI measurements of T1 relaxation rate and macromolecular tissue volume (MTV, introduced by Mezer, 
Yeatman and colleagues5). T1 measures of the longitudinal relaxation rate of hydrogen protons in a magnetic 
field and post mortem work has demonstrated that, in white matter, T1 is primarily driven by variation in myelin 
content88. The PI and collaborators have confirmed that qMRI measures of MTV are an accurate index of the 
true volume of tissue macromolecules within each voxel5. Thus, MTV can be used to directly monitor the creation 
of new tissue over the course of an intervention and, in combination with T1, can be used to make inferences as 
to the cellular composition (e.g., myelination) of tissue at millimeter resolution. Quantitative T1 and MTV mapping 
will be performed by collecting T1-weighted images (SPGRs) at multiple flip angles (1mm3 resolution) and 
transmit coil inhomogeneity (B1) will be corrected based on unbiased T1 estimates from a low resolution spin-
echo inversion recovery sequence4,5. T1 and MTV values will be mapped to fiber tracts using AFQ4. 
Combining qMRI and dMRI for a more accurate model of human brain tissue: The qMRI and dMRI 
measurements in Aim 1 are independent acquisitions and we have demonstrated in previous work that these 
measures are sensitive to independent biological processes in the white matter4. These measures provide a 
means to differentiate changes in intracellular water fraction from changes in myelin. For example, although the 
branching of astrocyte processes has a large impact on diffusion89, astrocytes are believed to have a smaller 
impact on T1 relaxation than do changes in myelin4. Given that we have confirmed that measures of T1 and 
diffusion have similar SNR4, and similar sensitivity to developmental change, the presence of common versus 
differential effects in the two modalities would point to distinct biological underpinnings. An interpretation of 
differential effects in the two measures will have to acknowledge and account for differential biases (e.g., subject 
motion introduces bias into diffusion, but not qMRI measures 4), but such a finding would be novel in the 
literature. In summary, combining multiple MRI techniques will lead to a more detailed description of underlying 
biological sources of white matter plasticity during learning and build new bridges with animal models. 

 
Research questions and preliminary data 
Aim 1.A: Does intervention cause short-term, transient changes in the white matter, or long-term 
remodeling of the brain circuit? Based on previous work, we find highly significant changes (p<0.0001) in 
diffusion properties for an extensive network of white matter tracts (Figure 2 A,B,C), and stability in a control 
group11. Surprisingly, even tracts beyond the core reading circuitry show dramatic learning-induced plasticity. 
Hence, there is no question that reading intervention causes rapid and widespread changes in white matter 
tissue properties. However, there are many potential interpretations for these changes that point to different 
underlying mechanisms. For example, we might interpret changes in mean diffusivity as reflecting increased 
“white matter integrity”, and infer that white matter deficits in children with dyslexia are remediated by the 
intervention program80. Alternatively, we might posit that the changes in mean diffusivity reflect transient changes 
in oligodendrocyte precursor cells (and other glial cells) resulting from the new experience of the intervention, 
but not reflecting permanent remodeling of the brain circuit. Under this second interpretation, white matter 
plasticity is still critical to the learning process (in line with animal work1,2,90), but reflects a more nuanced 
mechanism than typically considered in human studies. To dissociate these alternative accounts of experience-
dependent plasticity and dyslexia-remediation, we will analyze follow-up measurements at 6 and 12 months 
post-intervention to determine if: (a) diffusion properties return to baseline, (b) changes remain stable or continue 
to grow over time, or (c) diffusion properties remain stable or continue to grow only in tracts that are critical 
for skilled reading (e.g., left arcuate fasciculus and ILF), but return to baseline in other tracts (Figure 2D). 
These data will offer a new understanding of white matter plasticity and learning by either: (1) confirming the 
stability of rapid, learning-induced changes, or (2) dissociating short-term 
and long-term mechanisms of change. No study has examined the 
relationship between short-term and long-term mechanisms of 
plasticity, meaning that findings will be novel, irrespective of how the results 
Figure 2: Diffusion measurements detect highly significant, intervention-
driven white matter plasticity. LME models were used to model changes in mean 
diffusivity as a function of hours in the intervention (change/hr). (A,B) Significant 
linear growth was detected in a network of tracts including, but not limited to, the 
“core reading circuitry”. Linear growth rates are shown for each tract, color-coded 
based on the p-value from the model. There is an extensive literature linking a core 
network of tracts (including the left arcuate fasciculus and left ILF) to reading skills. 
Surprisingly, our data demonstrate rapid, intervention-driven changes in these 
tracts, as well as many other tracts that are not conventionally associated with 
reading. Based on a simulation conducted with these data, the proposed sample 
size has excellent power to reliably measure white matter plasticity: assuming a 
10% attrition rate, which is larger than the attrition in the pilot study, power = 0.98 
for the left arcuate and 0.95 for the left ILF at α = 0.05. (C) Mean diffusivity sampled 
along the trajectory of the ILF (from the occipital pole to the temporal pole) shows 
incremental growth between each measurement session, and along the full tract. 
Error bars represent +/- 1 SEM. Over the course of learning, many tracts show very 
similar growth trajectories to the one displayed for the ILF. (D) Why does such an 
extensive network show highly significant changes during the intervention? One 
hypothesis is that the structural changes seen during the intervention are transient 
and will return to baseline when the intervention is complete. Alternatively, we might 
hypothesize that only the tracts that are critical for maintaining the behavioral 
improvements, tracts that are considered to be the “core circuitry for reading” 34,35, 
will show sustained change or continued growth after the intervention is complete. 
This hypothesis is illustrated in panel D. During the baseline period we expect 
stability in the measurements: 19 control subjects not enrolled in the 
intervention showed no change over the course of 4 measurement sessions. 
Hence, changes are not attributable to development or repeated scanning. During 
the intervention period we observe plasticity in the core reading circuitry (orange) 
and other, non-core tracts (blue). At 6-month and 12-month follow up sessions, our 
“transient change hypothesis” predicts that many of the measures will return to 
baseline and match the dyslexic control group. Critical tracts that support the 
behavior are predicted to show continued growth, and this growth will also be 
reflected in changes in quantitative T1 and MTV values (see Aim 1.B). 

turn out. Given the high power for detecting short term learning effects (power = 0.95-0.99 at α=0.05), Aim 1.A 
is a low-risk, high-reward endeavor. 
Aim 1.B: Do different properties of the white matter display different time-courses of plasticity? We 
measured rapid changes in mean diffusivity that emerge within just a few weeks of intervention (Figure 2C). 
This is in line with studies reporting mean diffusivity changes after hours of learning89, and animal models indicate 
that these rapid changes are likely to reflect the morphology of glial cells. However, we cannot determine from 
diffusion measurements alone if changes in the diffusion signal are driven by glia (as suggested in the 
aforementioned work89), or if there are also changes in myelination and axon properties, as we might expect 
from recent animal work demonstrating that myelin plasticity is a critical component of learning1,2. By collecting 
long-term follow up measurements, and combining new, cutting-edge quantitative MRI pulse sequences and 
biophysical models, we will be able to better understand the underlying biology and time-course of the rapid 
white matter plasticity. We hypothesize that our data will fall in line with the sequence of changes that occur in 
mouse models of learning1,2: initial proliferation of glial cells throughout an extensive network, followed by long 
term remodeling of axons and myelin within the specific pathways that are critical for the behavior. The initial 
proliferation of glial cells would have a dramatic impact on mean diffusivity, but limited impact on T1, or MTV 
values4,89. Subsequent changes in myelin would have a dramatic impact on T1 and MTV values, but limited 
impact on m diffusivity. Though no MRI measure is specific to a single tissue type, combining multiple measures 
makes it possible to reason about the most likely biological source. 
Aim 1.C: Does white matter plasticity track behavioral improvements? Seminal work by Barres and 
colleagues demonstrated that electrical activity on an axon causes oligodendrocytes to respond with increased 
wraps of myelin91,92. This work inspired a surge of interest in understanding the role of white matter plasticity in 
learning75,93–95. But an outstanding question is the extent to which changes in the white matter reflect the process 
of remodeling the circuit to support changes in behavior, versus simply experience dependent changes that do 
not directly relate to learning of the new behavior. Recent work published in Science, demonstrated that activity-
dependent changes in myelin are fundamental for motor learning in a rodent model1,2. When receptors on 
oligodendrocytes were blocked, meaning that myelination remains constant but no new myelin is created in 
response to electrical signals, mice did not learn a complex motor task. While this work clearly demonstrates 
that myelin is critical for learning, it does not indicate that all changes in the white matter are important for 
learning. For example, one interpretation of our pilot data is that the new experiences associated with the 
intervention program promote cellular changes that are not tightly linked to behavioral improvements.  
Aim 1.C will investigate the link between white matter plasticity and behavior. Three statistical approaches will 
be employed. First, LME models will be used to test whether changes in diffusion properties, MTV and T1 track 
behavioral changes during the intervention. To limit the number of statistical comparisons, we will perform a 
principal component analysis (PCA) of the reading scores and use the first PC as a general index of reading 
abilities. For any significant effect, we will conduct post-hoc analyses to determine if it is specific to certain 
measures of reading skills. Pilot data show a statistically significant, but modest, relationship between the time-
course of MD change within a subject and improvements in reading score (r = -0.30 Arcuate, p = 0.003; r = -0.32 
ILF, p = 0.006). Second, correlation analysis will be used to analyze whether changes in white matter tissue 
properties between: (a) the baseline period and the long-term follow-up sessions and (b) the immediate post-
intervention session and the long-term follow-up sessions, correlate with individual differences in the amount of 
behavioral change. We hypothesize that the rapid changes in diffusion properties observed during the 
intervention are experience-dependent but do not reflect learning per se, and will only show a modest relationship 
to individual differences in learning. Instead it will be the longer-term changes in T1 values (indicative of 
myelination) that predict crystallized skill acquisition. Third, we will perform an exploratory analysis using latent 
change score modeling96–98 to investigate whether the data support time-lagged relationships between growth in 
the white matter and growth in reading skills. 
Alternative strategies 
Aim 1 is grounded in methods for which the PI’s lab has substantial expertise, and previous work indicates large 
effects with excellent statistical power. Additionally, there is superb support at the scanning facility, with an “on 
call” physicist, and technical staff that are trained to work with children. However, some of the more cutting-edge 
modeling approaches are still being refined in the literature (including work by our collaborators99,100) and are, 
therefore, open to alternative interpretations. We will confirm that our inferences do not depend on specific model 
assumptions by ensuring findings hold up under different modeling frameworks101–105. Additionally, even though 
the proposed interpretations of the qMRI measures are grounded in an extensive literature 4,5,88,99,106–114, these 
interpretations are simplifications of complex biophysical phenomena. For a comprehensive understanding of 

the link between measured changes in MRI signals, and underlying biological sources, we will leverage 
simulations115–118. Simulations are a powerful tool to explore alternative interpretations of in vivo measurements. 
Finally, we do not anticipate issues with subject attrition: Before enrolling in the intervention, potential subjects 
undergo extensive behavioral testing, MRI screening, and practice at an MRI mock scanner. Power is greater 
than 0.95 assuming a 10% attrition rate, but no subjects dropped out from the pilot study.  
Aim 2: Bottom-up and top-down computations in the reading circuitry 
Significance: When our eyes fixate upon a word, a cascade of 
neural processes is initiated, beginning in the visual system and 
progressing through a series of computations that translate the 
visual representation into sound and meaning. In skilled readers, 
this process occurs in a fraction of a second, and engages a well-
characterized network of brain regions119–123. In people with 
dyslexia, many regions within this circuit show lower levels of 
activation. However, simply knowing that a region is “under-
activated” does not elucidate the nature of the aberrant neural 
computations associated with poor reading. For example, in skilled 
readers a region of ventral occipitotemporal (VOT) cortex, termed 
the visual word form area (VWFA), selectively responds to printed 
text. This “text-selective” response is considered a hallmark of 
literacy, and under-activation of this region is the most consistently 
reported neural deficit in people with dyslexia16,122–130 (see16,124 for 
meta-analysis). Preliminary data on a small group of children scanned before and after the Seeing Stars 
intervention confirms that the intervention prompts a reliable increase in text-selectivity within the VWFA (Figure 
3). This learning-induced change was present in every subject. The text-selective response in the VWFA is 
typically interpreted as indicating neural tuning for orthography12,131–133, although it is also widely accepted that 
top-down signals contribute to the VWFA response15,17,134,135. Hence, there are different mechanisms that might 
contribute to differences in dyslexia, and changes with learning. By employing a paradigm that tightly controls 
both the visual properties of the stimulus, and the cognitive processes that are engaged by the subject17, it will 
be possible to re-interpret the mechanisms underlying low levels of activation. This aim leverages the first 
computational model of the VWFA17. By capitalizing on longitudinal measurements over the intervention, we will 
then determine how the learning process shapes neural computations in VOT.  
Innovation: In Aim 2 we employ an experimental paradigm that dissociates the automatic, bottom-up, stimulus-
driven response evoked by seeing a word, from the top-down effect of the cognitive processes engaged by the 
subject (Figure 4). Our previous work has demonstrated that when subjects are engaged in a difficult task that 
diverts attention from the visual stimulus, the VWFA BOLD response can be accurately predicted with a three 
parameter visual encoding model (Figure 4D)17. This model posits specific neural computations governing the 
VWFA response. However, these “bottom-up” computations fail to predict the VWFA BOLD response when 
subjects engage in a task that requires them to attend to the visual stimulus. For example, when subjects engage 
in a one-back task (blue line, Figure 4C), the VWFA response is 
Figure 
4: 
Bottom-up 
and 
top-down 
computations in the VWFA. (A) The VWFA is 
localized in ventral occipitotemporal cortex. (B) 9 
Subjects were shown 22 categories of images, 
and engaged in three different tasks while 
viewing the images (Fixation, Categorization, 
and One-back tasks). (C) Average BOLD 
responses 
are 
shown 
for 
the 
VWFA, 
demonstrating that the VWFA is sensitive to 
visual properties of the image, and cognitive 
demands of the task. The VWFA selectively 
responds to text in the absences of attention 
(Fixation-task), and the response is amplified in 
the One-back task. (D) A three-parameter model 
predicts the bottom-up VWFA response based 
on a series of computations that neurons perform 
on the visual image. Note: our previous work 
separated the VWFA response into bottom-up 
vs. top-down and here we specifically consider 
the nature of the top-down signals. 
Figure 3: Learning-induced changes in the 
VWFA response to text. (a) Text-selective 
response in the VWFA, indexed as the difference 
in response to words compared to other images, is 
highly correlated with reading skill (N=18). (b), A 
voxelwise analysis shows a highly significant 
increase in text-selectivity localized to the typical 
location of the VWFA (p<0.001). The bar graph 
shows the change in selectivity for each individual 
subject (red dots, N=5). 

amplified by 400% above the model prediction (black arrow, Figure 4C). Hence, there are separable components 
of the VWFA response: (1) bottom-up neural tuning properties and (2) top-down, task-dependent modulation. 
We refer to this task-dependent modulation of visual cortex as “top-down” since it reflects the cognitive demands 
of the task, but there are other interpretations that are consistent with the experimental design such as automatic 
versus goal-directed processing of the stimulus. Therefore, knowing that the VWFA response level differs 
between subjects, or changes with learning, could reflect a variety of mechanisms. Our innovative experimental 
approach will (a) provide new insights into the mechanisms underlying neural deficits in children with 
dyslexia16,125,127 and (b) dissociate intervention driven changes in cognitive processing, from intervention driven 
changes in the automatic processing of text. 
Relation to previous work: There is an extensive literature demonstrating that phonological processing regions 
in temporo-parietal cortex (TPC) show improved responses after intervention136–142. Some of these studies also 
report changes in VOT cortex138 but, since the myriad of fMRI intervention studies employ cognitive tasks that 
require reading and/or phonological processing, it is unclear whether the improved VOT response reflects: (a) 
top-down signals from phonological processing regions, (b) improved automatic response to words, or (c) a 
combination of multiple effects. Our study fills an important gap by: (a) targeting changes in specific computations 
within VOT cortex, (b) employing a unique array of tasks and stimuli designed based on a computational model 
of the VWFA. These data will also allow us to test the hypothesis that top-down signals from TPC during 
phonological encoding play an important role in tuning the bottom-up VOT cortex response to words126,143. 
Moreover, of the 22 fMRI intervention studies reviewed in142, most involve ~1hr/day of intervention making us 
uniquely poised to resolve the importance of intervention dosage for changing the VOT response.  
Functional MRI measurements of bottom-up and top-down computations in the reading circuit 
Data processing – A fundamental challenge in the longitudinal analysis of fMRI data is poor alignment due to 
EPI distortions. By collecting field maps at the beginning and end of each session, we will correct for field 
inhomogeneity, allowing for alignment of longitudinal data within ~1mm144,145. Distortion corrected fMRI data will 
be aligned to each subject’s MPRAGE scan and resampled to the cortical surface146. A general linear model 
(GLM) will be fit to the data with GLMdenoise147 leading to improved SNR. 
Experiment 1 (Localizer scan) – All analyses will be conducted within each individual’s native space, and 
without the use of smoothing kernels, allowing us to make precise inferences about neural response properties 
that are not confounded by anatomical differences among subjects. The use of an individual subjects approach 
will also allow us to identify mechanisms of change that would be obscured by a conventional group-average, 
voxelwise analysis (e.g., changes in the size of the VWFA relative to the immediately adjacent fusiform face area 
(FFA) vs. changes in VWFA selectivity for words).  
Word, face and object selective regions14,148–151 will be localized on each individual’s cortical surface based on 
an optimized, block design, localizer152. Based on these data we can determine whether the learning process 
affects the size, or location, of category selective regions, and localize regions of interest (ROIs) that will be 
analyzed in the subsequent experiments. Having distortion-corrected localizer data collected over time will allow 
for three analysis strategies: (1) If there are not systematic changes in the boundaries of each region, then ROIs 
will be defined by concatenating the data across the sessions to maximize SNR; (2) If the VWFA does not exist 
in session 1 data, and emerges by the final scan as reading skills improve, then ROIs will be identified based on 
the final scan data so that we can determine how response properties in this patch of cortex change over the 
course of learning; (3) If the size, location and boundaries of category selective regions change during learning, 
then we will model these changes using deformation fields to better understand learning-induced cortical 
reorganization. Finally, the use of template ROIs remains a viable alternative strategy. 
Experiment 2: Dissociating bottom-up encoding from top-down modulation – Data will be analyzed within 
regions of interest (see above) and, as an alternative strategy, group analysis will be used to examine effects 
that might occur outside of our a priori ROIs (see alternative strategies). Five image categories (words, faces, 
abstract objects, symbol strings and foreign characters will be presented at fixation (4 degrees visual angle, 
800ms image presentation, 200ms ISI). Each image category will be presented at 2 contrast levels (4% and 
100%) for a total of 10 stimulus conditions. Stimuli will be organized into 4 second blocks, and the order will be 
randomized with blanks to maximize our SNR for estimating the response to each stimulus type17,152,153. In recent 
work, we have developed a paradigm to separate bottom-up (stimulus-evoked) and top-down (task-dependent) 
signals in the VWFA17. On alternating runs with the exact same stimuli, children will engage in a Fixation task 
or One-back task. The Fixation task is designed to isolate the automatic, bottom-up response: the subject makes 
judgments about the color of rapidly changing fixation dot, ignoring the presented images, with task difficulty 

controlled by a staircase procedure to maintain an 82% level of performance. The One-back task is to press a 
button whenever any image appears twice in succession, and therefore directs attention to the images and, for 
words, engages the broader network of language regions involved in reading. We will employ the One-back task 
in this proposal, rather than a reading-specific task, because all children are able to perform the One-back task 
regardless of reading skill. The use of a reading-specific task would add the confound of differences in task 
performance: for example, in a severely dyslexic child, it would be unclear if lack of top-down modulation reflects 
a general deficit in top-down signaling, or the inability to perform the reading-related task. There is a large text-
selective response in both tasks (Figure 4). 
Experiment 3: Rhyming task – In order to (a) isolate top-down signals due to phonological processing and (b) 
examine functional connectivity between the VWFA and TPC, we will employ the rhyming task of Hoeft and 
colleagues38,125. This task involves rhyme judgments on visually presented words. By comparing the VWFA 
response in our novel visual encoding task to the VWFA response on this well-studied phonological processing 
task over the course of intervention we can test the hypothesis that top-down phonological signals are critical for 
VWFA tuning126. Moreover, we can relate functional connectivity between the VWFA and TPC to changes in 
white matter connectivity. 
Research questions 
Aim 2.A: To what extent do deficits in ventral regions reflect automatic, stimulus-driven, neural tuning 
properties versus top-down signals? Multiple competing mechanisms have been proposed to explain text-
selective responses in VOT: (1) the text-selective response reflects tuning properties of neurons in visual cortex 
that are involved in automatic word recognition131,154; (2) the text-selective response reflects top-down signals 
from language regions15,155. These alternate explanations of computations in the VWFA provide two very different 
interpretations for under activation of this region in people with dyslexia. Under activation might result from (a) 
poor tuning of neurons for text or (b) deficiencies in top-down signals originating from language regions. To 
achieve a more complete understanding of neural deficits in dyslexia, it is essential to tightly control both the 
cognitive task engaged by the subject, as well as properties of the visual stimulus to disambiguate visual 
encoding from task-related activation. No previous study has examined VWFA response properties using 
a fixation task that minimizes top-down signals. Though this group comparison is not the primary motivation 
for the proposed experiments (see 2.B and 2.C), the data will provide a more nuanced and mechanistic 
understanding of previously reported VWFA deficits in dyslexia. 
Aim 2.B: To what extent does intervention change the bottom-up, or top-down response in the VWFA? 
There is general agreement that a successful intervention improves the response to words in VOT cortex138,156. 
In an intervention study by Shaywitz and colleagues, intervention effects were initially observed in TPC, and 
changes in VOT were observed in long-term follow-up measurements157. This seminal study lead to the 
hypothesis that the process of remediating reading disabilities first improves phonological processing circuits 
that subsequently tune VOT for automatic word recognition. This hypothesis was formalized in a model by Pugh 
and colleagues, which posits a central importance of TPC in the development of VOT126,143,158. In line with this 
model, we hypothesize intervention-driven changes will initially manifest as top-down, task-dependent signals 
from phonological processing regions in TPC and no change in the bottom-up VOT response. As reading fluency 
improves towards the end of the intervention, and over the subsequent year, changes will crystalize as an 
improvement in the automatic, bottom-up response to words. In other words, we hypothesize that both bottom-
up and top-down signals play crucial roles at different points in the learning process. 
Pilot data in literate adults demonstrates that the bottom-up VWFA response is highly selective for words over 
other visual stimuli even when subjects are performing the Fixation task and ignoring the words (Figure 4 red 
line). Pilot data in a group of children performing a visual detection task on words, faces and objects confirms a 
strong correlation between reading skill and VWFA selectivity for words, even in the absence of tasks requiring 
reading or phonological processing (Figure 3a). In a small sample of 5 dyslexic subjects who initially did not 
show a text-selective response, the Seeing Stars intervention induced a reliable increase in the text-selective 
response for every subject (Figure 3b); the proposed experiments and modeling will allow us to determine the 
extent to which this change reflects bottom-up or top-down signals as well as resolving the time-course of 
learning effects.  LME models will be used to analyze longitudinal changes in the fMRI data, and to link changes 
in neural responses to changes in behavior. The pilot data confirms that we can expect an increase in the 
response to words over the course of the learning period (mean % BOLD increase = 0.15, SD = 0.034, bias 
corrected SD = 0.036, power > 0.95 at α=0.05), and the specific experiments will provide a nuanced 
understanding of the neural computations that produce this change in BOLD amplitude. 

Aim 2.C: Relating changes in structure to changes in function. Do changes in white matter connectivity 
increase the efficiency of top-down signals and improve functional connectivity between visual and 
phonological processing regions? The tight control provided by a within-subjects experimental paradigm 
provides a unique opportunity to model the relationship between white matter tissue properties and cortical 
response properties in the reading circuitry. For example, we might posit that increases in top-down, task-
dependent modulation of the VWFA arises from increased efficiency of white matter connections. Indeed, we 
have demonstrated a direct connection from the VWFA to TPC, and inferior frontal cortex (IFC)159, meaning that 
top-down signals might emanate from these classic language circuits. In our previous work, we introduced a 
method to model how specific brain regions modulate the VWFA response17. Here we will capitalize on this 
approach to model the strength of top down signals from (a) TPC, (b) posterior parietal cortex and (c) IFC to the 
VWFA. We will then examine whether a subject’s intervention-driven change in 3 white matter tracts predict 
changes in top down signal strength: (a) the posterior arcuate fasciculus, (b) the vertical occipital fasciculus and 
(c) the arcuate fasciculus. Modeling the relationship between the white matter and cortical computation is of 
great theoretical importance and we consider sub-aim 2.C as a high-risk high-reward endeavor. This level of risk 
for a sub-aim is appropriate given the exceptional statistical power for each of our other aims and sub-aims.  
Alternative strategies 
Group analysis: Our approach benefits from the increased precision, specificity, and power afforded by an ROI 
analysis160,161. But voxel-wise group analysis remains a perfectly viable alternative strategy. A study-specific 
template will be created with the ANTS toolbox162, de-noised data will be normalized to the template and 
voxelwise statistics will be computed in line with our aims. This alternative approach may lead to discoveries in 
other components of the reading circuitry beyond our specific hypotheses about VOT cortex.  
Aim 3 – Neural biomarkers of learning outcomes 
Significance: Given the commitment that is required for a successful 
intervention, ideally, we would have accurate methods to predict the amount of 
improvement a child a child is likely to show. Predicting a child’s response-to-
intervention (RTI) has been a major focus of the behavioral intervention 
literature. Numerous predictor variables have been examined with PA and RN 
emerging as the most consistent predictors31,32. However, despite progress in 
understanding the child characteristics that predict intervention success or lack 
thereof, prediction accuracy is far from perfect. With substantial room for 
improvement, many labs have turned to neuroimaging as a potential means to 
improve prediction. For example, in our pilot data, dMRI measures of the 
arcuate predict RTI better than PA or RN (Figure 4B). In the long run, an 
understanding of the mechanisms that predict individual differences in 
learning will pave the way for innovative, personalized intervention programs 
that specifically target these mechanisms.  
In the neuroimaging literature, convergent findings, across multiple labs, 
have identified tissue properties of the arcuate fasciculus and superior 
longitudinal fasciculus (SLF) as potential biomarkers for dyslexia by 
demonstrating that tissue properties: (a) correlate with pre-reading skills such 
as phonological awareness23, (b) differentiate preschool children and infants 
at risk for dyslexia from not-at-risk individuals33,41,163, (c) develop more rapidly 
in good versus poor reading children36,37, and (d) predict future reading gains 
in elementary school children with poor reading skills36,38. What is particularly 
noteworthy is that arcuate/SLF tissue properties predict future reading 
development better than any behavioral measure36,38. However, none of 
these studies have assessed whether tissue properties are predictive 
of behavioral gains in an intervention setting. Assessing the predictive 
power of this biomarker in a controlled intervention setting is both important for ruling out the possibility that 
predictions were influenced by environmental differences among children, and for the practical purpose of 
developing tools to predict a child’s likelihood of success before the substantial commitment involved in this 
popular intervention program. The successful development of a predictive model can then be extended to other 
intervention programs through our sharing of reproducible analysis tools6,61.  
Figure 
4: 
Arcuate 
diffusivity  
predicts 
reading 
improvement. 
Examining the region of the arcuate 
identified by Wang and colleagues 36, 
we find that pre-intervention diffusion 
properties predict each child’s rate of 
improvement 
on 
the 
TOWRE. 
Children with lower diffusivity showed 
greater RTI. Using a step-wise, 
multivariate regression model to 
compare arcuate diffusivity, PA, and 
RN as predictors of RTI, only arcuate 
diffusivity 
was 
retained 
as 
a 
significant predictor. In other words, 
this 
biomarker 
subsumes 
the 
predictive value of the most widely 
used behavioral predictors. 

Innovation: The concept of “neuroprognosis”, or using brain measurements to make useful and prognostic 
predictions, has generated a lot excitement, and a flurry of publications in the field of educational neuroscience 
(for review see39). However, one of the limiting factors is that there is always substantial variability in the quality, 
and type, of education that children have access to. This sets an upper-bound on the potential accuracy of any 
prediction: even if the critical brain property is identified for a particular academic skill, a substantial portion of 
the variance in outcome measurements will be due to differences in a child’s environment. Indeed, roughly 50% 
of the variance in reading skills is the product of environmental factors164. The current proposal benefits from an 
intervention design in which environmental factors are controlled across subjects, allowing us to test whether 
there are neuroanatomical factors, unique to the child, that predispose them to succeed in the intervention 
program. Our approach to prediction will begin by examining biomarkers that are motivated by the literature and 
pilot data. After establishing the predictive value of these theory-driven biomarkers, we will use cutting-edge 
machine learning algorithms to investigate whether a multivariate, data-driven approach can improve 
prediction accuracy. Our approach to neuroprognosis benefits from unique tools we have developed to quantify 
tissue properties in the individual, and leverages collaboration with machine learning experts to explore the high-
dimensional space afforded by these data. 
Research questions and preliminary data 
Aim 3.A: Tissue properties of the arcuate as a biomarker for response to intervention: The aforementioned 
studies demonstrating that arcuate tissue properties are predictive of longitudinal growth employed the AFQ 
software package33,36, which is developed by the PI’s lab. Hence, the proposed research team is optimally suited 
to follow up on these findings, develop methods for automated, brain-based predictions of reading outcomes, 
and disseminate these tools to the field. Wang and colleagues reported that a specific region of the arcuate is 
predictive of reading outcomes when children are followed longitudinally in a conventional school setting36. 
Examining this same region of the arcuate, we find that each subject’s pre-intervention diffusivity values 
predict their improvement during the intervention above and beyond baseline behavioral measures 
(Figure 4). These data are a promising example demonstrating that biomarkers identified in one laboratory, can 
be replicated and extended by another lab, and lend support to the perspective that anatomical properties of the 
arcuate play a role in determining how easily children learn reading skills. Such an extension is made possible 
through open sharing of code6,165, and is facilitated by the open science approach of the PI’s lab.  Pilot 
data clearly establishes the feasibility of predicting learning outcomes based on properties of the arcuate (power 
= 0.93 for RD; 0.99 for AD at α = 0.05). The critical question is whether this biomarker predicts long-term follow-
up measurements (above and beyond variance predicted by baseline behavioral data), since long-term 
achievement is the ultimate goal of an intervention. This type of long-range, post intervention prediction has not 
been examined before in the neuroimaging literature. Cross validation will be used to test out of sample 
generalization and assess whether biologically specific qMRI parameters improve prediction accuracy.  
Aim 3.B: Does the severity of neural processing deficits predict resistance to remediation? Even though 
the Seeing Stars intervention produces highly significant changes in reading skills, there is variability among 
children, with some children increasing by more than 2 standard deviations, and others showing limited growth. 
Is variability in response to intervention linked to the severity of underlying neural deficits? We will focus on three 
regions implicated in dyslexia: the temporoparietal region involved in phonological processing166,167, the ventral 
occipitotemporal VWFA, and the inferior frontal region that has been proposed as a compensatory mechanism 
in dyslexia38,125–127. The pre-intervention response profile in each of these regions will be used to index the 
magnitude of neural deficits in each subject and will be examined as a predictor of RTI.  Aim 3.B is considered 
an exploratory analysis due to lack of supporting pilot data. However, the data for Aim 3.B will already be 
collected for Aim 2, and it is of theoretical importance to know whether more severe neural deficits, in specific 
components of the reading circuitry, are resistant to remediation. 
Alternative strategies 
Even though our approach to prediction is grounded in an extensive literature, we have to acknowledge the 
possibility that the prediction accuracy will not generalize beyond the specifics of previous samples. We contend 
that a rigorous test of biomarkers proposed in the literature is important and we will, therefore, 
preregister our study such that the results are published irrespective of prediction accuracy168. 
Machine learning: Aim 3.A confronts specific hypotheses that are grounded in previous work but, as an 
alternative approach, we will capitalize on new machine learning algorithms to explore combinations of 
neuroanatomical features that might be used to predict RTI. This alternative approach will be supported through 
collaboration with Data Scientist Ariel Rokem at the UW eScience Institute. Our approach to machine learning 

will start with feature extraction that is informed by the scientific literature and regularized regression: data from 
3.A and 3.B will be organized into a predictor matrix and (1) linear combinations of features that predict reading 
improvements  (e.g., using Elastic Net169,170), as well as non-linear combinations (e.g., using Random Forest 
Regression171) will be learned from the data. We can incrementally increase the size of the predictor set by 
adding (a) data from voxels rather than average ROI responses (akin to MVPA38), and (b) data from all nodes in 
the tracts rather than averaged for regions of tracts. PCA will be used for dimensionality reduction165,172. Cross-
validation will be used to control for over-fitting and ensure out of sample generalization at each stage. 

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Recruitment and Retention Plan 
We will recruit 80 (40 intervention, 40 control) children between 9 and 11 years of age from the University of 
Washington Reading and Dyslexia Research Database (UW-RDRD). The UW-RDRD is a subject pool 
that is maintained by the PI’s laboratory. It includes hundreds of children in the Seattle area who are 
interested in participating in reading research and have undergone extensive behavioral testing and 
questionnaires. Currently about 1,000 children are enrolled in the database and there is an enrollment 
rate of 200-300 subjects per year. This resource supports the timely recruitment of specific samples of 
subjects including children with dyslexia and as proposed in the present study. We don’t anticipate any 
issues with subject recruitment: due to the popularity of our previous NSF-funded intervention study there 
are dozens of that have already requested to be put on a wait-list for future intervention studies after the 
previous study was fully enrolled. 
The Yeatman Lab has been very successful in retaining subjects for longitudinal studies. For example, the 
previous intervention study (see Pilot data in Research Strategy) involved 4 scans collected longitudinally 
and there was no subject attrition. This success has been achieved by hiring laboratory personnel who 
are good with children and their families. All of our research assistants have years of experience working 
with children and are exceptional at making sure families feel that their commitment to research is valued, 
and that children enjoy coming in for their visits. Research assistants are knowledgeable about the 
scientific studies they work on and engage children and their families in the process of scientific research. 
In our experience, by instilling a genuine interest in the research, families feel engaged, committed, and 
excited to continue participating in our studies. 
Protection of Human Subjects 
1 - Risks to the Subjects  
1a - Human Subjects Involvement, Characteristics and Design We will recruit 80 children (9-11years of age) 
from the University of Washington Reading and Dyslexia Research Database (UW-RDRD). The UW-
RDRD is a subject pool that is maintained by the PI’s laboratory. It includes hundreds of children in the 
Seattle area who are interested in participating in reading research and have undergone extensive 
behavioral testing and questionnaires. This resource supports the timely recruitment of specific samples 
of subjects. 
For all subjects, inclusion criteria include having no major contraindication for MRI (braces, metal implants, 
pacemakers, vascular stents, or metallic ear tubes). Because the study involves measurements of reading 
and language ability, new recruits will be native English speakers. Subjects have no history of neurological 
disorder, significant psychiatric problems or ADHD diagnosis. We also exclude claustrophobic subjects 
since an MRI might be uncomfortable for them.  
40 subjects will be recruited for the intervention study and 40 subjects will be recruited as control subjects. As 
described in detail in the Research Strategy Intervention subjects will be children between nine and eleven 
years of age who are below the 25th percentile in timed (TOWRE composite) and untimed (WJ Basic 
Reading Skills) single word decoding abilities, and within the normal range (+/- 1 SD) on measures of 
general cognitive abilities (WASI). Of the control subjects, half will be matched in terms of reading skills 
and half will have typical reading skills (see Research Strategy). 
1b – Study procedures, materials and potential risks  
All data obtained in this study is obtained for research purposes. Sources of research material include: 1) 
screening information, 2) behavioral tests, and 3) MRI data. All of the data obtained is confidential and 
research reports never use data from a named individual. When data are coded for computer analysis, all 
participants' names are kept separately by research staff and findings made available only to legitimate 
agents of the participant (e.g., personal physician, etc.) with the permission of the participants or legal 
guardian.  
The experience gained through previous research projects on reading skills in children has helped the PI develop 
secure, efficient, and useful techniques for data handling. For each subject that enters the database, their 
data is split between two separate, password-protected data stores connected by a unique identifier All 
identifying information is stored in a registry that is used for maintaining contact with subjects and 
coordinating consent procedures. Data, including online questionnaires and reading scores from lab visits, 
is stored in a de-identified repository. Moreover, a folder is created that contains all cognitive tests and 

imaging data. This folder and the repository serve as a permanent archive of original subject data. 
Extensive precautions are taken to insure the privacy of subjects and the confidentiality of data. 
Specifically, subject identity is numerically coded on all pages within subject folders and in the database. 
All subject folders are kept in confidential, locked filing cabinets at the Institute for Learning and Brain 
Sciences. The database is password-protected and is hosted on a University of Washington server. Only 
personnel directly associated with the grant have access to subject information. Subject background 
information including name, gender, ethnicity, and relevant medical and personal information is kept in 
the registry, which represents a centralized, restricted access location that is separate from the behavioral 
and imaging results. This data handling system is efficient, guarantees subject confidentiality, and serves 
research needs expeditiously.  
There are no risks to individuals from the interviewing, testing (paper-and- pencil or computer). There is minimal 
non-significant risk (i.e., "research not involving greater than minimal risk" as defined by DHHS) 
associated with the MRI scanning procedure. The MRI machine uses a strong magnet and radio frequency 
magnetic fields to make images of the brain. The magnetism and radio frequency magnetic fields do not 
cause harmful effects at the levels used in the MRI machine.  
2 - Adequacy of Protection Against Risks  
2a - Recruitment and informed consent 
All child subjects are recruited from King County, and surrounding area. Flyers, ads in local newspapers, and 
notices placed in school, community, electronic bulletin boards and reading clinics serve as the primary 
means of recruitment into the UW-RDRD. We then invite subjects to participate in specific studies based 
on meeting the specific study enrollment criteria. The study is explained in detail to all potential 
participants. Parental guardians of all human subjects included in this study sign a written, informed 
consent. For children, informed consent is obtained from the subject and the subject's legally authorized 
representative (parent or legal guardian). Consent forms are written in language understandable to the 
subjects and their representatives, and subjects are allowed sufficient opportunity to consider whether or 
not to participate so as to minimize the possibility of coercion or undue influence. An investigator or 
research assistant explains the study to the child and a parent or guardian who is asked whether they 
have concerns with participation in the study. If any hesitation is noted, the subject is not included in the 
study.  
All subjects are told that their participation is voluntary, and that during the study they may withdraw from the 
research at any time. Some children may want to withdraw from the study while their parents feel that they 
have incentives to participate in the study. During the prescreening as well as the scanning sessions, we 
query children both in the absence and presence of their parents. If they show less enthusiasm or they 
are reluctant when their parents are not present, we discontinue participation. Subjects are also informed 
that reluctance to participate does not in any way compromise the availability of their health care or their 
eligibility to participate in other studies. Also, once the study has begun, subjects can change their mind 
at any time about whether they wish to continue in the project. This does not affect their medical care. 
Written consent will be documented with University of Washington IRB-approved consent and assent 
procedures.. Remuneration for expenses related to participation in the study will be offered at $60 for a 
session involving neuroimaging and $20 for behavioral sessions. All subjects in this study will be informed 
if any medically important information is learned from their research participation that may significantly 
affect their current diagnosis or treatment, or influence their willingness to continue participation in this 
study.  
2b - Protection against risks  
The consent forms list any possible risks to the MRI scanning procedure. The operators of the MRI at the MRI 
center are well trained to use protocols and procedures to ensure that the MRI equipment is used correctly 
to minimize any risks. The imaging coils and software are also tested for safety. National and UW 
guidelines have been developed for these machines, and these recommendations will be strictly followed.  
Rigorous attempts will be made to desensitize participants to the MRI scan acquisition procedure as described 
in Research Design and Methods. Participants who have not experienced an MRI scan previously will 
undergo training on the UW MRI simulator, which acclimate them to the actual MRI scanning procedure. 
However, some subjects may still feel anxious before or during the MRI scan. We will continually assess 

for any sign of discomfort during implementation of the research protocol. Subjects can alert the console 
operator that they need help or need to ask a question by squeezing a hand-held device during the scan. 
Scans will be terminated at a subject's request or if the console operator or individuals in the room detect 
significant subject discomfort. To date, the PI has had significant success in scanning children and adults. 
3 - Potential benefits of proposed research to human subjects and others  
Individuals participating in this study as intervention subjects will receive a high-quality reading intervention 
program free of charge. Based on our previous research, this intervention is likely to lead to substantial 
improvements in reading skills. Individuals participating in this study as control subjects may not receive 
any direct benefits. The general benefit to medical science will be in furthering our understanding of the 
neurobiological mechanisms underlying the developmental of skilled reading and the development of 
personalized reading intervention programs. It is our belief that the potential benefits from this study to the 
advancement of scientific knowledge (and, therefore, indirectly to participants and their families), 
substantially outweigh the minimal risk to human subjects. Children may learn important reading skills 
over the course of the study. 
4 - Importance of the knowledge to be gained  
This research offers promise in furthering our understanding of both brain function and structure in children. The 
studies proposed will provide new opportunities to explore fundamental issues of cognitive neuroscience 
that relate cognitive functions to brain organization. It will provide the foundation for treatment and 
remediation options for individuals with dyslexia, and elucidate the mechanisms of learning. 
Data and Safety Monitoring Plan 
The study involves behavioral testing (paper and pencil, computer and interview) and MRI scanning and all 
procedures involve minimal non-significant risk. The intervention also involves no risk to the subjects: 
participants work one-on-one with a trained instructor to practice reading related skills much like a typical 
educational setting. Our safety monitoring is commensurate with this minimal level of risk and, as outlined 
in the Protection of Human Subjects, involves ensuring the safety and confidentiality of each of the 
procedures (e.g., screening for metal before the MRI). The aspect of this study that makes it a clinical trial 
is the reading instruction program, and we have taken care to design a procedure to ensure the fidelity of 
the intervention approach. This will fidelity monitoring will be carried out by co-Investigator Roxanne 
Hudson and PI Jason Yeatman. This fidelity monitoring plan is laid out in the Research Strategy as it is a 
critical component of the research methodology: 
Intervention protocol: Intervention will be administered to participants in Lindamood-Bell learning centers in 
the Seattle area four hours a day, five days a week, for eight consecutive weeks during summer vacation. 
Trained tutors will work one-on-one with each child. The tutors will be trained employees of Lindamood-
Bell, who have extensive experience with the intervention protocol and have completed a 3-week training 
program (see more below). Research subjects will be provided with the Seeing Stars intervention free of 
charge (see Lindamood-Bell letter of support) and the curriculum will be administered in Lindamood-Bell 
learning centers but with extensive and independent monitoring by trained personnel on the research 
team (see Fidelity Monitoring below). 
Importantly, the scientific research will be completely independent of the administration of the intervention to 
eliminate any potential conflicts of interest. Lindamood-Bell will not have access to any information the 
PI’s laboratory collects on the subjects, including subject attrition, subject testing results which are 
conducted in the PI’s laboratory or MRI data collection. 
The Lindamood-Bell “Seeing Stars” instruction program teaches phonological and orthographic processing 
through a combination of mental imagery and sensory-motor learning. It is laid out in detail in the Seeing 
Stars manual which is publicly available and has been extensively described in other publications. 
Children practice visualizing the orthography of words, starting from simple consonant-vowel syllables and 
systematically working into more complex consonant-vowel-consonant pairings. The instruction focuses 
on segmenting and blending strings of phonemes into words and visualizing the relationship between the 
articulatory elements, and their representation as visual symbols. Phonological awareness is 
systematically built through articulatory exercises where children are taught to attend to the relationship 
between motor movements of the mouth and tongue, and speech sounds in words, and the corresponding 
letters. As children build a stronger foundation of phonological and orthographic knowledge, additional 

practice with increasingly complex sight-word identification and phonological decoding is layered into the 
intervention.  
Intervention fidelity and monitoring: The intervention curriculum is detailed in the publicly available Seeing 
Stars Teacher’s Manual allowing the procedure to be precisely reproduced. Fidelity to the published 
curriculum will be ensured in two ways. First, Lindamood-Bell has established a rigorous methodology for 
ensuring the quality and fidelity of instruction: (1) Each instructor completes a 3-week training program; 
(2) At each learning center a “coach” with multiple years of experience monitors each instructor’s progress 
and provides feedback, ensuring that the instructor is properly progressing through the steps of the 
curriculum with their student; and (3) Each coach provides weekly reports to the regional director to ensure 
consistency of implementation across learning centers. This fidelity monitoring will be recorded and 
databased for each research study participant. Second, one session a week (for each child) will be 
recorded with a digital video camera and a member of the research team (independent of Lindamood-
Bell) will score the fidelity of implementation for each session based on: (1) adherence to the procedures 
of the intervention using a 5-point implementation behavior scale and (2) engagement of the child. Child 
engagement will be measured using a 5-second partial interval time sampling procedure on the same 
videotaped session used for procedural fidelity. Reliability procedures will require research staff to rate a 
sample of training videos and for their ratings to correlate at r ≥ .80 prior to data collection. The researcher 
will provide regular feedback to the Lindamood-Bell regional director based on the independent 
assessment of fidelity so that instructors can receive coaching in the case of deviations from the published 
curriculum. Finally, scoring will be databased so that details of the intervention delivery can be examined 
as covariates in statistical models of intervention effects. Roxanne Hudson, a member of the research 
team with extensive experience monitoring and ensuring the fidelity of educational interventions will be 
primarily in charge of the monitoring procedures and will work closely with Yeatman and the rest of the 
research team to ensure the procedures are carried out faithfully. 
Statistical Design and Power 
Aim 1: Aim 1 targets the relationship between white matter plasticity and learning and seeks to quantify changes 
in the white matter that occur with intervention. There will be 40 intervention subjects and 40 control 
subjects. Linear mixed effects models will be used to compare white matter changes during the 
intervention period to (a) the control period and (b) the control group. Simulations based on pilot date in 
26 subjects indicate exceptional statistical power: 0.98 for the arcuate fasciculus (primary tract of interest) 
and 0.95 for the inferior longitudinal fasciculus (secondary tract of interest) at alpha=0.05. This power 
analysis takes into account an expected 10% attrition rate. In the pilot study there was no subject attrition. 
Additional details are provided on page 6 (see Figure 2 caption) of the Research Strategy. 
Aim 2: Aim 2 targets changes in the computations performed by the visual word form area, a region that is critical 
for rapid and automatic word recognition. Functional magnetic resonance imaging data will be used to 
measure the selectivity for words compared to other stimuli in this region. This aim uses the same subjects 
as Aim 1. Linear mixed effects models will be used to compare white matter changes during the 
intervention period to (a) the control period and (b) the control group. Based on pilot data, a power analysis 
confirms excellent statistical power: mean % BOLD increase = 0.15, SD = 0.034, bias corrected SD = 
0.036, power > 0.95 at α=0.05. Additional details are provided on page 10 of the Research Strategy. 
Aim 3: Aim 3 develops a predictive model of individual differences in learning within the intervention group (40 
subjects) and is, therefore, not listed as a clinical trial outcome measure. There is still excellent statistical 
power for accomplishing this aim: regression analysis will be used to predict individual differences in 
reading skill improvement and pilot data demonstrates statistical power of 0.96 (at α = 0.05) for our primary 
predictor (left arcuate fasciculus diffusivity). Additional details are provided on page 11 of the Research 
Strategy and in Figure 4. The analysis for Aim 3 will also be pre-registered to avoid publication bias. 
Machine learning will then be used to test the hypothesis that prediction accuracy can be improved 
(compared to the aforementioned regression analysis) based on learning linear and non-linear 
combinations of features. This is considered an exploratory sub-aim and we have defined a detailed plan 
to use cross-validation to control for over-fitting and ensure out of sample generalization at each stage.