In summary, S has displayed delayed physical development and delayed puberty. Despite medications, S's physical development has never fully recovered, being at the 5th-10th percentile in weight (43.5Kg) and less than the 5th percentile in height (136.5cm) at time of scanning. For comparison, her weight of 43.5Kg is typical of a 12-12.5 year-old, while her height of 136.5cm is typical of a 9.5-10 year old girl. [Norms from ]. S's head size was 3% larger than the average of our control group at the time of testing.
Despite requiring these significant medical interventions, S did not miss a considerable amount of school. Her initial diagnosis occurred when she was 4 years of age. Therefore, her expected enrollment into a small private pre-school was delayed by one year, to the age of 5. She began kindergarten at 6 years of age, missing only four months at the end of kindergarten due to medical interventions. She started the 1st Grade the following year and completed the year without any interruption. The only other school interruption occurred in December of 2nd Grade but was limited to approximately three weeks around the winter break. From 2nd Grade until the present (S is currently in 11th Grade) there have been no interruptions in her educational experience. In summary, we would not expect her profound dyslexia and other cognitive deficits to be due to missing several months of kindergarten, although subtle effects cannot be ruled out.
White matter damage following radiation therapy was initially progressive, as was the development of large cystic regions in the left frontal and parietal lobes (compare Figures 1 and 2). S has also suffered several cognitive problems. Her most profound deficits are in reading acquisition and visuospatial processing, despite extensive reading remediation as well as math interventions. Since Kindergarten she has been in the public school system, and an Individual Education Program (IEP) allows for reading and math services during the day. She is in regular classes with an individual aide for all other subjects. S has undergone several speech and language screenings by the school district but never qualified for services.
S participated in the Lindamood-Bell  (LB ) Learning Processes (Lindamood-Bell, San Luis Obispo, CA) programs on multiple occasions. LB 's programs are based on the premise that reading requires the integration of sensory-cognitive functions and skills. S received a combination of two of their programs: 1) LiPS  (Lindamood Phonemic Sequencing for Reading Spelling and Speech), which promotes phonemic awareness, and 2) Seeing Stars  (Symbol Imagery for Sight Words, Reading, Phonemic Awareness, and Spelling), which targets word identification and reading fluency. In the fall of 1999, when S was in the 3rd grade, she had one semester of LB instruction 2 hours per day. By January of 2000 she had received 60 hours of LiPS  and 105 hours of SI . In the summer of 2000, after 3rd grade, S attended an LB clinic 4 hours per day for 6 weeks and received a total of 58 hours of LiPS  and 58 hours of SI . In the 8th grade (2004), she was administered LB for 2 hours per day for one semester and received 160 hours of SI . In the 9th grade (2005), she was administered one year of LB for 2 hours each day, and received 310 hours of SI . In the 10th grade, she received assistance with writing strategies by an LB consultant.
Pre- and post-testing of S's reading skills were assessed using the Gray Oral Reading Test-3 (GORT-3) (Widerholt and Bryant, 1994) and Gray Oral Reading Test-4 (GORT-4), the Wide Range Achievement-Test-3 (WRAT-3) Reading subtest, the Woodcock Reading Mastery Test-Revised (WRMT-R) Word Attack subtest. Examining all reading scores it is evident that S demonstrated a flat learning curve based on her raw scores and a widening gap between her and same age peers over time based on her age corrected percentile scores (Table 1).
When S was in the 9th grade, her mother administered Read Naturally, a computerized reading fluency program. The participant practices reading the same stories multiple times with the goal of increasing reading speed. S practiced at the 1st grade level and improved her speed on average by 55 words per minute to 69 words per minute. The program is not designed to improve reading accuracy. There was no formal pre- and post-testing administered. In 10th grade she began a computerized math intervention program (Math Concepts and Skills 2) from SuccessMaker  Enterprise (Pearson Digital Learning, Pearson Education Inc., Phoenix, AZ) for one class per day; she also worked on it at night and on the weekends. The computer program started at the 3.0 grade level and was adjusted to the 3.5 grade level after administering a series of calculations to S. S spent approximately 83 hours on this program which covered computations (addition, division, multiplication, and subtraction) and applications (geometry, measurement, number concepts, probability and statistics, problem solving, science applications, word problems). By the end of the program S's cumulative performance was at the 4.87 grade level. There was no formal pre- and post-testing administered.
S is a right-handed female who was 15 years, 11 months at the time of our neuropsychological evaluation and DTI and fMRI scans. She had previously undergone neuropsychological evaluation at age 11 and a psycho-educational evaluation at age 13. These past assessments were notable for verbal abilities within the average range with significantly lower visuospatial and visuoperceptual skills, including well below average constructional skills. In these assessments there was no indication of difficulty with complex language comprehension, though speech was reported as "mildly dysnomic." More precise understanding of S's dysnomia is not known because no scores were listed in the report, but her dysnomia is not severe enough to be noticeable in normal conversation. Her memory was described as generally adequate with no marked distinction between verbal and visual memory. Problem-solving skills were reported to be within the average range. Her academic skills in reading and math were well below average. The neuropsychological assessment at age 11 was comprised of the following: Beery Test of Visuomotor Integration, Boston Naming Test, California Verbal Learning Test, three subtests (not specified in the report) of the Children's Memory Scale, Facial Recognition Test, Token Test, Rey Osterrieth Figure, the Wechsler Individual Achievement Test, and the Wechsler Intelligence Scale for Children-III (WISC-III). The only raw scores provided in the report of the neuropsychological evaluation were the WISC-III. All available test scores are presented in Table 2.
Consent from the mother and assent from the child were obtained at the beginning of the study, consistent with the approved human subjects protocol by the Stanford Panel on Human Subjects in Medical Research.
Our neuropsychological evaluation of S consisted of the following measures: Laterality-Edinburgh Handedness Inventory (EHI); Intellectual Functioning- Wechsler Intelligence Scale for Children-IV (WISC-IV); Achievement- Woodcock-Johnson Tests of Achievement-III (WJ-III), the GORT-4, Test of Word Reading Efficiency (TOWRE); Phonological Processing-Comprehensive Test of Phonological Processing (CTOPP); Language-Clinical Evaluation of Language Fundamentals-4 (CELF-4), Controlled Oral Word Association Test, Animal Naming Test, Boston Naming Test-2; Executive Function- Wisconsin Card Sorting Test; Memory- Wechsler Memory Scale-III.
Diffusion-weighted imaging (DWI) data were acquired on a 1.5T Signa LX (Signa CVi, GE Medical Systems, Milwaukee, WI) using a self-shielded, high performance gradient system capable of providing a maximum gradient strength of 50mT/m at a gradient rise time of 268mus for each of the gradient axes. A standard quadrature head coil, provided by the vendor, was used for excitation and signal reception. Head motion was minimized by placing cushions around the head and securing a Velcro strap across the forehead.
The DWI protocol used eight 90-second whole-brain scans; these were averaged to improve signal quality. The pulse sequence was a diffusion-weighted single-shot spin-echo, echo planar imaging sequence (TE =63ms; TR = 6s; FOV = 260 x 260 mm; acquisition and reconstruction matrix size = 128 x 128; bandwidth = +-110kHz; partial k-space acquisition). We acquired 56 axial, 2 mm thick slices (no skip) for b = 0 and b = 800 s/mm2. The higher b-value was obtained by applying gradients along 12 different diffusion directions (six non-colinear directions). Two gradient axes were energized simultaneously to minimize TE. The polarity of the effective diffusion-weighting gradients was reversed for odd repetitions.
DTI data were pre-processed using a custom program that removed eddy current distortion effects and determined a constrained non-rigid image registration using normalized mutual information. The six elements of the diffusion tensor were determined by multivariate regression. The eigenvalue decomposition of the diffusion tensor was also computed.
For each subject, the T2-weighted (b=0) images were coregistered to the corresponding T1-weighted 3D SPGR anatomical images. The coregistration was initiated using the scanner coordinates stored in the image headers to achieve an approximate alignment. This alignment was refined using a mutual-information 3D rigid-body coregistration algorithm from SPM2. Several anatomical landmarks, including the anterior commissure (AC), the posterior commissure (PC) and the mid-sagittal plane, were identified by hand in the T1 images. With these landmarks, we computed a rigid-body transform from the native image space to the conventional AC-PC aligned space. The DTI data were then resampled to this AC-PC aligned space with 2 mm isotropic voxels using a spline-based tensor interpolation algorithm, taking care to rotate the tensors to preserve their orientation with respect to the anatomy. The T1 images were resampled to AC-PC aligned space with 1 mm isotropic voxels. We confirmed by visual inspection of each dataset that this coregistration technique aligns the DTI and T1 images to within 1-2 millimeters in the brain regions of interest. However, geometric distortions inherent in EPI acquisition limit the accuracy in regions prone to susceptibility artifacts, such as orbito-frontal and anterior temporal regions.
We used standard non-linear spatial normalization methods from SPM2 to align the 54 control brains and build a template from them (see). The diffusion data for subject S had significant geometric distortions in the cerebellum and inferior occipital lobe due to the spinal fusion hardware in her neck. For this reason, we independently aligned her T1-weighted images and her DTI data to the control template brain. S's T1-weighted images were aligned to the control brain T1-weighted template. Her DTI data were normalized to this same coordinate space by aligning her non-diffusion-weighted EPI data to the template EPI. The resulting non-linear deformation was applied to the tensor field, taking care to rotate the tensors to preserve their orientation with respect to the anatomy by converting the local deformation field at each voxel to an affine transform and then applying the preservation of principal direction algorithm to the tensor at each voxel.
The final result of these image manipulations produced a good alignment both between S's T1 and tensor data and between her brain and that of the 28 controls, as assessed by careful inspection. However, some severe artifacts due to the metal implants remained in the lower part of S's tensor data, especially in the cerebellum. Therefore, we restricted our analyses to the axial plane at or above the anterior commissure (Z>=0), which was about one centimeter above the artifact region.
Because we had suspected that certain pathways might be missing in subject S's brain, we did not want to compute the spatial normalization based on the diffusion-weighted data [e.g., using a method such as FSL's TBSS ]. Such methods are useful for finding diffusion differences between corresponding pathways but are not designed to find differences that might include the presence or absence of pathways.
We used an auditory covert verb generation paradigm to measure language related brain responses in S and identify her lateralization for language stimuli. Auditory presentation was used to overcome S's reading difficulties. During the scans, S was asked to covertly generate verbs for each presented noun, and listen attentively to noise stimuli. We confirmed, using a short behavioral test, that S could perform the task overtly at a presentation rate of one word every three seconds. Experimental stimuli consisted of high frequency English nouns, presented aurally through MRI compatible headphones (Avotec, Inc., Stuart, FL). Control stimuli were auditory noise patterns created by randomizing the phase of the word stimuli, while maintaining their amplitude envelope. Each condition was repeated in six 15s blocks (consisting of 5 stimuli, ISI=3s) presented in pseudorandom order and interleaved with 15s rest periods. The whole experiment was divided into 2 short runs, 207s per run. Continuous MR data acquisition was used during these runs.
FMRI measurements were performed on a 3T General Electric scanner with a custom-built volume head coil. Head movements were minimized by padding and tape. Functional MR data were acquired with a spiral pulse sequence. Twenty-six oblique slices were prescribed approximately along the AC-PC plane, covering the whole temporal lobe and most of the frontal lobe (3 mm thick, no gap). The sampling rate of the BOLD signal (TR) was 3s and the voxel size was 2.5 x 2.5 x 3 mm.
In-plane anatomical images were acquired before the functional scans using a fast SPGR sequence. These T1-weighted slices were taken at the same location of the functional slices, and were used to align the functional data with the high-resolution T1 anatomical data acquired on the 1.5T scanner during the DTI scan session. The spiral sequence used for the fMRI was much less affected by the spinal fusion hardware in S's neck than the EPI-based DTI sequence, so no additional steps were necessary to obtain adequate alignment between the fMRI data and the subject's T1-weighted anatomical images.
FMRI data were analyzed using the freely-available mrVista tools (http://white.stanford.edu/software/). Images acquired from the functional scans were inspected for motion by an experienced analyst, who concluded that excessive motion in the second run (>5mm) was not corrected by our motion compensation procedure. Therefore the analysis was restricted to the first run. Our results thus rely on one functional run in which each condition was presented in 3 blocks. This is not uncommon in neurological patients and constitutes the main reason for using a powerful task that is known to activate language regions robustly even with very short scans. Baseline drifts were removed from the time series by high pass temporal filtering. A small amount of spatial smoothing (with a 3 mm FWHM Gaussian kernel) was applied to the data to enhance sensitivity to small signal changes. A general linear model (GLM) was fit to each voxel's time course, estimating the relative contribution of each condition (verb generation, auditory noise) to the time course. GLM predictors were constructed as a boxcar for each condition convolved with a standard hemodynamic response model. Statistical maps were computed as voxel-wise t-tests for each predictor and between the weights of the two predictors. For visualization purposes, the statistical maps were interpolated to the high resolution volume anatomy.