(Open Access) Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children (2011) | Jason D. Yeatman

Q: What have the authors contributed in "Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children" ?

The primary objective of this study was to use diffusion tensor imaging to examine the relationship between individual variation in the microstructural properties of arcuate fibers and behavioral measures of language and reading skills. Contrary to previous investigations that report the absence of the right arcuate in some subjects, the authors demonstrate that new techniques can identify the pathway in every individual.

Anatomical Properties of the Arcuate Fasciculus Predict

Phonological and Reading Skills in Children

Jason D. Yeatman

, Robert F. Dougherty

, Elena Rykhlevskaia

Anthony J. Sherbondy

, Gayle K. Deutsch

, Brian A. Wandell

and Michal Ben-Shachar

Abstract

■

For more than a century, neurologists have hypothesized

that the arcuate fasciculus carries signals that are essential for

language function; however, the relevance of the pathway for

particular behaviors is highly controversial. The primary objec-

tive of this study was to use diffusion tensor imaging to examine

the relationship between individual variation in the microstruc-

tural properties of arcuate fibers and behavioral measures of

language and reading skills. A second objective was to use novel

fiber-tracking methods to reassess estimates of arcuate laterali-

zation. In a sample of 55 children, we found that measurements

of diffusivity in the left arcuate correlate with phonological

awareness s kills and arcuate volume lateralization correlates

with phonological memory and reading skills. Contrary to pre-

vious investigations that report the absence of the right arcuate

in some subjects, we demonstrate that new techniques can

identify the pathway in every individual. Our results provide

empirical suppo rt for the role of the arcuate fascicul us in the

development of reading skills.

■

INTRODUCTION

In his seminal m edical diss ertation, Carl Wernicke pro-

posed the first network model of language processing

in the brain (Wernicke, 1874). His view of cortical com-

putations portrayed the brain as a mosaic of sensory and

motor representations, where new functions arise from

novel interactions among these regions. Wernicke ob-

served that lesions in the left posterior superior temporal

cortex (Wernickeʼs area) produced a deficit in the ability

to process speech input. Recognizing that lesions in left

posterior inferior frontal cortex (Brocaʼs area) create a

deficit in the ability to produce speech, he later proposed

that th e arcuate fasciculus (AF) is essential for commu-

nicating phonological and motor information between

these two language regions. Wernicke, therefore, hypoth-

esized that a lesion to fronto-temporal white matter con-

nections should produce impaired speech repetition with

relatively spared speech production and comprehension,

a syndrome later confirmed and termed conduction apha-

sia (Catani & Mesulam, 2008; Catani & Ffytche, 2005;

Lichtheim, 1885).

Wernickeʼs model was later a dopted by G eschwind,

who advocated the disconnection hypothesis: Deficits

in high-order cognitive functioning are the result of le-

sions that interrupt the communication between sensory

and motor regions. Geschwind presented a series of neuro-

logical cases that supported Wernickeʼs hypothesis, con-

firming the relationship between an arcuate lesion and

conduction aphasia (Geschwind, 1965a, 1965b, 1970) The

Wernicke–Geschwind hypothesis was further supported

by Damasio and Damasio (1980) based on computed to-

mography measures of six patients with impaired speech

repetition, who showed consistent damage to the insula

and posterior superior temporal gyrus (STG) as well as

the underlying white matter, which is assumed to include

the AF.

The functional role of the AF and the specific func-

tional and anatomical deficits characteristic of conduction

aphasia have been subject to much controversy. In par-

ticular, recent diffusion tensor imaging (DTI) studies and

case reports show that arcuate lesions do not necessarily

result in conduction aphasia (Bernal & Ardila, 2009;

Rauschecker et al., 2009). These studies challenge the

hypothesis that signals essential for repeating the sounds

of speech are uniquely carried by the AF.

What are the implications of these changing perspec-

tives regarding the functional role of the AF in the healthy

brain? Altho ugh the findings from conduction aphasia

show that the AF may not be the unique pathway for

speech repetition, its anatomy and neurological history

suggest that it is likely to be one of a set of pathways that

the brain uses for phonological processing. This view is

expressed in recent models of language processing. For

example, Hickok and Poeppel propose that the AF maps

acoustic features of speech to articulatory motor represen-

tations, and they propose that the signals carried within

the AF are used for the manipulation and articulation of

Stanford University,

Bar Ilan University, Ramat Gan, Israel

incoming phonological information (Hickok & Poeppel,

2004, 2007). Thi s view is supported by studies that im-

plicate AF signals for reactivating phonological material

in verbal working memory (Friedm ann & Gvion, 2003)

and short-term storage and verbal repetition of speech

(Glas ser & Rillin g, 2008; Ri lling et al., 2008; Saur et al.,

2008; Catani & Ffytche, 2005). These theoretical models

of AF function are based on behavioral deficits in con-

duction ap hasics and the f unctio nal role of the cortical

regions connected by the arcuate. However, there is very

little research on the relationship between variance in AF

anatomy in healthy subjects and variance of the particular

skills that are hypothesized to depend on the signals car-

ried by AF axons.

Beyond models of language processing, models of read-

ing have recently featured the AF. Whole-brain voxel-based

group analysis of white matter diffusion properties found a

group difference between adult good and poo r readers

and a cor relation between white matter m icrostructure

and reading skills, located in the vicinity of the AF (Supple-

mentary Figure 1; Klingberg et al., 2000). The existence of

a microstructural white matter difference between good

and poor readers has been independently replicated in

children by several laboratories (Niogi & McCandliss, 2006;

Beaulieu et al., 2005; Deutsch et al., 2005). Although the

reading-related differences are located medial to the AF

and not in the AF itse lf, t he findings may still be inter-

preted as stemming from interdigitating, medial AF fibers

(Ben-Shachar et al., 2007; Beaulieu et al., 2005). If this

is true, it is possible that the AF contributes to reading

based on its role in phonological processing, because pho-

nological skills are an essential factor in reading devel-

opment (Wagner & Torgesen, 1987).

Most of these studies applied whole-brain voxel-based

methods, searching for group differences and correlations

between DTI measures and reading skill. Whole-brain

methods are valuable in the absence of a specific hypoth-

esis, but they lack anatomical specificity, because the same

voxel in a normalized brain may map to different tracts

across individuals. A further limitation of whole-brain

methods is reduced statistical power because of the large

number of statistical comparisons. Hence, whole-brain

methods are not optimal for testing specific anat omical

hypotheses.

Here, we capitalize on recent advances in fiber tracking

methods to identify the AF as an ROI in individual sub-

jects. We then test the specific hypothesis that the diffu-

sion measures within this tract covary with standardized

measures of phonological memory, phonological aware-

ness, and reading skills. We further explore how laterality

of the tract relates to behavior and how the subjectʼssex

modulates these effects t o follow up on recent reports

(Lebel & Beaulieu, 2009; Catani et al., 2007; Lenroot et al.,

2007). Our results provide empirical support for the hy-

pothesis that the AF is part of the reading circuitry and

that maturation of the arcuate is important for the devel-

opment of reading-related skills.

METHODS

Subjects

Fifty-five children aged 7–11 years participated in this study.

The data analyzed here represent the first measurement

in a longitudinal study of reading development. Partici-

pants were physically healthy and had no history of neuro-

logical disease, head injury, attention deficit/hyperactivity

disorder, language disability, or psychiatric disorder. Screen-

ing for attention deficit/hyperactivity disorder was based

on the childʼshistoryandontheConnersʼ Parent Rating

Scale (Revised Short Form), which confirmed that all sub-

jects scored in the normal range (<65). All participants

were native English speakers and had normal or corrected-

to-normal vision and normal hearing. The Stanford Panel

on Human Subjects in Medical and Nonmedical Research

approved all procedures. Written informed consent/assent

was obtained from all parents and children.

Behavioral Testing

Participants completed a 4-hr battery of cognitive tests to

characterize their reading skills, phonological awareness,

rapid naming, and general intelligence. Descriptive statistics

on this sample have been previously reported (Dougherty

et al., 2007). On the basis of our specific hypotheses regard-

ing the AFʼs role in phonology and reading, we included

the following three age-standardized behavioral measures

in this study:

(a) Phonological Memory: The phonological memory

composite score m easures an individualʼs ability to

code information phonologically for temporary storage

in working memory. This score is composed of two

subtests: digit span and nonword repetition (Wagner,

Torgesen, & Rashotte, 1999).

(b) Phonological Awareness: The p honological aware-

ness composite score measures an individualʼs ability

to parse the word into syllables and phonemes and

manipulate these phonemes to make up new words.

The score is composed of two subtests that measure

the ability to segment and blend phonemes (elision

and blending; Wagner et al., 1999).

assesses a childʼs accuracy at reading single words

and pseudowords. It is composed of two subtests:

word identification, which measures accuracy in read-

ing aloud a list of words (untimed), and word attack,

which measures accuracy in reading aloud a list of

pseudowords (untimed; Woodcock, McGrew, & Mather,

2001).

DTI

MRI data were acquired on a 1.5-T Signa LX scanner (Signa

CVi; GE Medical Systems, Milwaukee, WI) using a self-

shielded, high-performance gradient system. A standard

Yeatman et al. 3305

quadrature head coil, provided by the vendor, was used

for excitation and signal reception. Head motion was mini-

mized by placing cushions around the head and securing

a strap across the forehead.

Data Acquisition

The DTI protocol used eight repetitions of a 90-sec whole-

brain scan. The scans were averaged to improve signal

quality. The pulse sequence was a diffusion-weighted single-

shot spin-echo EPI sequence (echo time [TE] = 63 msec;

repetition time [TR] = 6 sec; field of view [FOV] = 260 mm;

matrix size = 128 × 128; bandwidth = ±110 kHz; partial

k-space acquisition). We acquired 60 axial, 2-mm-thick slices

(no skip) for two b values, b =0andb = 800 sec/mm

.The

high b-value data were obtained by applying gradients

along 12 diffusion directions (six noncollinear directions).

Two gradient axes were energized simultaneously to

minim ize TE, and the polarity of the effective diffusion-

weighting gradients was reversed for odd repetitions to

reduce cross-terms between diffusion gradients and imaging

and background gradients. Although Jones (2004) suggests

that measuring more diffusion directions might be more

efficient at reliably estimating diffusion tensors of arbitrary

orientation, our signal-to-noise ratio is sufficiently high from

our eight repeats to produce very reliable tensor estimates.

We have confirmed this in a subset of subjects by comparing

bootstrapped tensor uncertainty estimates from 40 direc-

tion data with the 12 directi on data reported here. With

our high signal-to-noise ratio (SNR), tensor uncertainty

is limited by physiological noise rather than measurement

noise.

We also collected high-resolution T1-weighted anatomi-

cal images for each subjec t using an 8-min sagittal 3-D

spoiled gradient recall (SPGR) sequence (voxel size = 1 ×

1 × 1 mm). T he following anatomical landmarks were

defined manually in the T1 images: the anterior commis-

sure (AC), the posterior commissure (PC), and the mid-

sagittal plane. With these landmarks, we used a rigid

body transform to convert the T1-weighted images to the

conventional AC–PC aligned space.

Data Preprocessing

Eddy current distortions and subject motion in the diffusion-

weighted images were r emoved by a 14-parameter con-

strained nonlinear coregistration ba sed on the expected

pattern of eddy current distortions given the phase encode

direction of the acquired data (Rohde, Barnett, Basser,

Marenco, & Pierpaoli, 2004).

Each diffusion-weighted image w as registered to the

mean of the (motion-correcte d) non-diffusion-weighted

(b = 0) images using a two-stage coarse-to-fine approach

that maximized the normalized mutual information. The

mean of the non-diffusion-weighted images was automat-

ically aligned to the T1 image using a rigid body mutual

information algorithm. All raw images from the diffusion

sequencewereresampledto2-mmisotropicvoxelsby

combining motion co rrection, ed dy current correction,

and anatomi cal alignment transforms into one omnibus

transform and resampling the data using trilinear interpo-

lation based on code from SPM5 (Friston & Ashburner,

2004).

An eddy cu rrent intensity correction (Rohde et al.,

2004) was applied to the diffusion-weighted images at

the resampling stage.

The rotation component of the omnibus coordinate

transform was applied to the diffusion-weighting gradient

directions to preserve their orientation with respect to

the resampled diffusion images. The ten sors were then

fit using a robust least squares algorithm designed to re-

move outliers from the tensor estimation step (Chang,

Jones, & Pierpaoli, 2005). We computed the eigenvalue

decomposition of the diffusion tensor, and the resulting

eigenvalues were used to compute for fractional anisot-

ropy (FA; Basser & Pierpao li, 1996). The FA is th e nor-

malized standard deviation of the three eigenvalues and

indicates the degree to which the isodiffusion ellipsoid is

anisotropic (i.e., one or two eigenvalues are larger than

the mean of all three eigenvalues). The mean diffusivity

is the mean of the three eigenvalues, which is equivalent

to one third of the trace of the diffusion tensor.

We confirmed that the DTI and T1 images were aligned

to within a few millimeters in the ROIs for this study. This

confirmation was done by manual inspection by one of the

authors (R.F.D. ). In regions prone to susceptibility arti-

facts, such as orbito-frontal and inferior temporal regions,

the misalignment was somewhat larger because of uncor-

rected EPI distortions.

All the custom image processing software is available

as part of our open-source mrDiffusion package (revision

2289) available for download from vistalab.stanford.edu/

vistawiki/index.php/Software.

Fiber Tract Identification

To test the primary hypothesis, we manually identified the

left AF for each individual. We developed three alternative

methods to test the robustness and specificity of the re-

sults. First, we used an automated tract identification pro-

cedure to identify the arcuate. The main results were

evaluated on the tracts identified by the manual and auto-

mated procedures. Second, we identified control tracts in-

cluding the left superior longitudinal fasciculus (SLF), left

corona radiata, and right AF to confirm the specificity of

effects in the left AF. Third, we developed a tract align-

ment procedure to coregister anatomically equivalent por-

tions of the left AF and reduce confounds caused by

crossing fibers, tract curvature, and partial voluming with

neighboring structures. These three procedures are de-

scribed in detail in the Supplementary Material.

Figure 1 shows the three main steps of the manual AF

segmentation procedure: tracking, restricting by a coarse

ROI, and manual editing.

3306 Journal of Cognitive Neuroscience Volume 23, Number 11

Tracking. We manually identified the left AF in each

individual based on a DTI atlas of hum an white matter

(Mori, Wakana, van Zijl, & Nagae-Poetscher, 2005). We

seeded the tracking algorithm with a mask of all left hemi-

sphere voxels with an FA value of greater than 0.2 (Basser,

Pajevic, Pierpaoli, Duda, & Aldroubi, 2000; Mori, Crain,

Chacko, & van Zijl, 1999). Fiber tracts were estimated us-

ing a deterministic streamlines tracking algorithm (Basser

et al., 2000; Mori et al., 1999) with a fourth-order Runge–

Kutta path integration method and 1-mm fixed-step size. A

continuous tensor field was estimated with trilinear inter-

polation of the tensor elements. Starting from initial seed

points within the white matter mask, the path integration

procedure traced streamlines in both directions along the

principal diffusion axes. Individual streamline integration

was terminated u sing two standard criteria: tracking is

halted if (1) the FA estimated at the current position is be-

low 0.15 or (2) the minimum angle between the last path

segment and next step direction is >50°.

Restricting by a coarse ROI. The ROI was defined based

on red green blue (RGB) map that color codes the prin-

cipal d iffusion direction (PDD) within each voxel: red

for left–right, green for anterior–posterior, and blue for

superior–inferior. On each subjectʼs RGB map, we manu-

ally defined an ROI that encompassed all green voxels lat-

eral to the internal capsule, between MNI plane z =20

and z = 30 (Catani, Jones, & Ffytche, 2005; Figure 1A).

This ROI was defined liberally, making sure to include all

possible arcuate voxels and allowing voxels from neighbor-

ing tracts as well. The left hemisphere fiber group was lim-

ited to those fibers that intersected the ROI.

Manual editing. We manually sel ected fiber s that (1)

turned inferior at the temporal parietal junction and en-

tered the temporal lobe and (2) continued anterior at the

central sulcus and entered the frontal lobe (Figure 1C).

We eliminated fibers that (1) headed ventrally toward

the insula, (2) turned medially toward the corpus callo-

sum or medial frontal cortex, or (3) turned superiorly

for the middle and superior frontal gyri. This procedure

was implemented using a gesture-based interface (Akers,

2006). Th e procedure identified all the left hemisphere

fibers that projected from the temporal lobe dorsally over

the Sylvian fissure to the inferior frontal and precentral

gyri, corresponding to the AF (Figure 1D).

Calculating fiber tract summary measures.Weex-

tracted the three tensor eigenvalues at each point along

each fiber. From these, we calculated FA, mean diffusiv-

ity, radial diffusivity (RD), and axial diffu sivity (AD) and

averaged each measure along the entire tract. This method

effectively computed a weighted-average because voxels

with greater fiber density contributed more to the final

measure than voxels with low fiber density. This weighting

reduced the effects of partial voluming, because the fiber

density is related to the likelihood that a voxel is filled with

arcuate fibers.

We tested correlations between FA in the left arcuate

and phonological memory, phonological awareness, and

basic reading skills. For significant FA correlations, we ex-

amined RD (the mean of the second and third eigenvalues)

and AD (the first eigenvalue) to determine which aspect of

the diffusion characteristics best predicted behavior.

Arcuate Volume Estimation

We estimated the volume of the arcuate in two ways to

ensure that significant findings reflect meaningful biologi-

cal variation and are not dependent on our specific proce-

dure. First, we obtained a volume estimate as a weighted

sum of the number of voxels containing arcuate fib ers.

Each voxelʼ s c ontribution to the volume estimate was

weighted by the ratio of arcuate fibers in the voxel com-

pared with fibers from adjacent tracts in the voxel. This

method reduces potential bias introduced by partial vol-

uming with neighboring tracts, such as the frontal-parietal

SLF. Second, we used the common method of simply

Figure 1. Method of identifying the AF in a single subject. Whole-brain streamlines tracing technique (STT) tractography produced a large collection

of estimated tracks (not shown). An ROI in the white matter was drawn comprising the voxels with an anterior–posterior (green) PDD that are

located adjacent and lateral to the cortical spinal tract. The cortical spinal tract can be identified because its PDD is in the inferior–superior (blue)

direction. (A) The PDD at each voxel in a typical axial slice (Z = 26). The white outline shows the region selected in this slice. The ROI is large

so that all of the AF fibers will be included. (B) The complete ROI for this subject, which is selected from several adjacent planes. (C) Estimated

fiber tracks passing through the ROI. We identify the AF fibers from the group by selecting the fibers that (1) project anterior to the central sulcus and

(2) continue posterior and inferior into the temporal lobe (red lines). These waypoints were manually identified for each subject in an interactive

fiber tract viewing and segmentation tool available for download at vistalab.stanford.edu/software. (D) The estimated left AF for this subject.

Yeatman et al. 3307

counting the estimated streamlines in each hemisphereʼs

tract (Lebel & Beaulieu, 2009; Catani et al., 2007).

In some subjects, deterministic tractography fails to

identify the right AF. This might imply that the right AF

is absent (Lebel & Beaulieu, 2009; Catani et al., 2007), or

alternatively, it might be explained by limitations of the

deterministic algorithm in regions of crossing fibers (Wahl

et al., 2010). For example, partial voluming between the

right AF and branching SLF fibers could send deterministic

tracking algorithms in the wrong direction. We used a new

algorithm that combines probabilistic methods and quan-

titative predictions of the diffusion data to test the hypoth-

esis that the right AF is missing (Sherbondy, Matthew, &

Alexander, 2010; Sherbondy, Dougherty, Ananthanarayanan,

Modha, & Wandell, 2009; Sherbondy, Dougherty, Ben-

Shachar, Napel, & Wandell, 2008). The new method iden-

tifies the most likely candidate streamlines by evalu ating

the agreement between the pathway and the diffusion mea-

surements. See Supplementary Method for more details.

RESULTS

The Left AF Tracts and Their Cortical

Projection Zones

Figure 2 shows the distribution of AF cortical endpoints.

For each subject, the endpoints were transformed into a

common coordinate frame, and we calculated the num-

ber of subjects with fiber endpoints within 3 mm of each

vertex on the cortical surface. Thus, the color overlay rep-

resents the likelihood, across subjects, of AF termination

point at each cortical location.

The frontal lobe projection zone of AF fibers is fairly com-

pact, falling mainly in the premotor cortex and Brodmannʼs

area 44. The temporal lobe projection zone differs widely

between subjects. Many subjects have AF branching fibers

across the superior, middle, and inferior temporal gyri,

whereas others have compact AF terminations in the pos-

terior tempor al lobe. The arcuate is a complex tract that

contains contributions from a wide array of cortical re-

gions, and the microstructure properties estimated within

the core of the arcuate probably contain contributions

from axons originating in a large part of cortex.

AF Diffusion Measurements Correlate with

Phonological Awareness

Averaged across the entire left AF, FA is negatively corre-

lated with phonological awareness (r = −.33, p =.01;

Figure 3A). This correlation arises because children with

better phonological abilities have greater RD (r = . 30,

p =.02)thanchildrenwithpoorphonologicalabilities

(Figure 3B), but AD (r = −.02, p = .88) is independent

of phonological ability (Figure 3C). The magnitude of the

correlation is the same for men and women, and it is also

the same for younger (7–9 years) and older children (10–

11 years). Furthermore, the correlation remains significant

after covarying for age. The principal correlation we ob-

serve is between RD and reading skills, and we examine

the finding in a number of different ways.

First, we considered whether a lternative methods of

identifying the AF might change the result. We identified

the AF using an automated procedure to remove any pos-

sible experimenter biases (see Methods). The AF identi-

fied using this method was similar. The FA (r = .85), RD

(r = .95), and AD values (r = .82) esti mated using the

two segmentation methods were highly correlated but

not identical. Even so, the FA and RD of the automatically

segmented tracts was significantly correlated with phono-

logical awareness scores (r =.3,p = .03). Hence, the

Figure 2. Cortical endpoints

of the left AF in 55 children.

Manually identified left AF fiber

groups for three representative

children are shown on the

left. The frontal lobe endpoints

are generally focused in

the precentral gyrus and

Brodmannʼs area 44 of the

inferior frontal gyrus, whereas

the temporal lobe endpoints

are spread over significantly

more surface area. The heat

map on the right shows the

number of subjects with left

AF endpoints at each region

of the cortex. Endpoints for

each subject were registered

onto the cortical surface of an

individual child.

3308 Journal of Cognitive Neuroscience Volume 23, Number 11

Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children

Figures

Citations

Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification

Illiterate to literate: behavioural and cerebral changes induced by reading acquisition

Beyond the arcuate fasciculus: consensus and controversy in the connectional anatomy of language.

A qualitative and quantitative review of diffusion tensor imaging studies in reading and dyslexia

Development of white matter and reading skills

References

The cortical organization of speech processing

The basis of anisotropic water diffusion in the nervous system – a technical review

Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI

Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging.

The nature of phonological processing and its causal role in the acquisition of reading skills

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Q1. What have the authors contributed in "Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children" ?