cluster tbi chapter 2013

8/10/2019 Cluster TBI Chapter 2013

1/29

95D.N. Allen and G. Goldstein (eds.), Cluster Analysis in Neuropsychological Research:

Recent Applications, DOI 10.1007/978-1-4614-6744-1_5,

Springer Science+Business Media New York 2013

Introduction

Traumatic brain injury (TBI) continues to be one of the most common causes of

disability and death among children and adolescents in the United States each

year, with many going on to have permanent disabilities (Faul, Xu, Wald, &

Coronado, 2010; Rivara et al., 2012). However, outcomes vary dramatically such

that some children demonstrate minimal long-term impairment, while others

evidence significant continuing disability. The classification of injury severity is

important then, because it may provide one means of predicting long-term out-

comes and prescribing treatment. In this way, severity classification may assist

in identifying those children who are at increased risk for long-term disability

following TBI and suggest specific interventions that might assist in the recovery

process.

Chapter 5

Classification of Traumatic Brain Injury

Severity: A Neuropsychological Approach

Daniel N. Allen, Nicholas S. Thaler, Chad L. Cross, and Joan Mayfield

D.N. Allen, Ph.D. (*) N.S. Thaler, M.A.

Lincy Professor of Psychology, Department of Psychology, University of Nevada Las Vegas,

4505 Maryland Parkway, Box 455030, Las Vegas, NV 89154, USA

e-mail: [email protected]; [email protected]

C.L. Cross, Ph.D., P.Stat, L.C.A.D.C., M.F.T.

Veterans Health Administration, Office of Informatics and Analytics, Las Vegas, NV, USA

School of Community Health Sciences, University of Nevada, Las Vegas,

4505 Maryland Parkway, Las Vegas, NV 89154, USAe-mail: [email protected]

J. Mayfield, Ph.D.

Our Childrens House at Baylor, 3301 Swiss Avenue, Dallas, TX 75204, USA

e-mail: [email protected]
http://-/?-http://-/?-http://-/?-http://-/?-


2/29

96

Despite its importance, classifying the severity of TBI has presented unique

challenges from very early on. For some disorders, where neuropathology is rela-

tively uniform across patients and where disease progression follows a relatively

well-defined course, classification efforts have been successful in staging the dis-

ease as it progresses from mild to severe, including a prototypical characterization

of both symptom expression and cognitive decline. In contrast, for disorders where

neuropathology is not uniform or heterogeneous, classifying the severity of brain

injury or dysfunction has been particularly challenging. In Chap. 3 the case for

schizophrenia is discussed, which although not an acquired disorder or typically

considered a neurological condition, is characterized by heterogeneity across a

number of domains, such as symptoms and outcomes. More relevant to the current

discussion is the observation that schizophrenia is also characterized by heteroge-

neous neurocognitive deficits. As is apparent from studies of schizophrenia, cluster

analysis can be particularly useful in identifying profiles of performance on neuro-psychological testing that may be related to important disorder-related variables

such as treatment outcomes, medication response, and longer-term prognosis. In the

current chapter, literature is reviewed that demonstrates cluster analysis is a useful

approach to investigate neurocognitive heterogeneity present in TBI and the case if

made for the potential usefulness of tests such as the Trail Making Test (TMT) to aid

in classifying the severity of the injury.

Heterogeneity and TBI Severity Classification

Saatman et al. (2008) recently underscored the problem that heterogeneity poses to

the classification of TBI through conventional methods. Their paper summarized

the preliminary deliberations of a workgroup tasked with developing a classification

system for TBI. They state, The heterogeneity of traumatic brain injury (TBI) is

considered one of the most significant barriers to finding effective therapeutic inter-

ventions with a pressing need to .develop a reliable, efficient, and valid classi-

fication system for TBI that could be used to link specific patterns of brain andneurovascular injury with appropriate therapeutic interventions (p. 719). While the

focus of their efforts was primarily on developing classification for therapeutic

interventions, their point is more generally relevant to classification for other pur-

poses, such as to predict educational and vocational outcomes, which is similarly

troubled by heterogeneity. Heterogeneity in outcomes arises from a number of

sources, and variability in neuropathology resulting from TBI is a major contributing

factor. This heterogeneity can be seen in Fig. 5.1, which presents computed tomog-

raphy scans (CTs) of six individuals who sustained a severe TBI. Each case high-

lights a different pathology, ranging from localized contusions to diffuse axonalinjury, which in turn may or may not be indicative of subsequent cognitive impair-

ment and functional disability. Furthermore, some children who sustain injuries

with little to no corroborating evidence from neuroimaging indicating the presence

of cerebral damage may experience substantial declines, while others with profound

D.N. Allen et al.
http://dx.doi.org/10.1007/978-1-4614-6744-1_3http://-/?-http://-/?-http://-/?-http://dx.doi.org/10.1007/978-1-4614-6744-1_3


3/29

97

visible injuries go on to make adequate recoveries (Suskauer & Huisman, 2009).

Therefore, classification methods should incorporate both clinical and neuroimag-ing data as well as subsequent assessments of behavioral and cognitive functions,

which are typically obtained through neuropsychological evaluation.

Current Classification Approaches

As detailed by Saatman et al. (2008) and others, there are a number of different

approaches to classify TBI. For example, pathoanatomic classification focuses oncommon neuropathological features of the injury, including both lesion location

and the underlying causative processes. This approach has identified four general

types of neuropathology associated with TBI that include (1) hematomas, (2) sub-

arachnoid hemorrhage, (3) contusions, and (4) diffuse axonal injury. To the extent

Fig. 5.1 Heterogeneity of severe traumatic brain injury (TBI).Note: computed tomography (CT)

scans of six different patients with severe TBI, defined as a Glasgow Coma Scale score of 8, high-

lighting the significant heterogeneity of pathological findings. CT scans represent patients with

epidural hematomas (EDH), contusions and parenchymal hematomas (contusion/hematoma), dif-

fuse axonal injury (DAI), subdural hematoma (SDH), subarachnoid hemorrhage and intraventricu-

lar hemorrhage (SAH/IVH), and diffuse brain swelling (diffuse swelling) (From: Saatman, K. E.,

Duhaime, A. C., Bullock, R., Maas, A. I., Valadka, A., & Manley, G. T. (2008). Workshop Scientific

Team and Advisory Panel Members. Classification of traumatic brain injury for targeted therapies.

Journal of Neurotrauma, 25(7), 719738. Used with permission. All rights reserved)

5 Classification of Traumatic Brain Injury Severity: A Neuropsychological Approach
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


4/29

98

that neuropsychological tests are differentially sensitive to these various types of

neuropathology, they may prove useful as part of a multidimensional classification

system.

The physical mechanism causing TBI has also been used to classify injury sever-

ity. This approach utilizes magnitude and direction of forces acting on the brain to

predict pattern of injury (Gennarelli & Thibault, 1985) and classifies injuries based

on whether they were caused by the head striking or being struck by an object

(impact loadings) or from the brain moving within the intracranial space (inertial

loadings). It has been observed that inertial loading is often associated with diffuse

injuries (e.g., DAI), while impact loading is more often associated with focal inju-

ries (brain contusion), providing some support for the validity of this approach.

Others have classified brain injury by distinguishing between primary and second-

ary injuries resulting from specific pathophysiological mechanisms, where primary

injuries result directly from the trauma (e.g., contusions), while secondary injuriesdevelop following the initial injury as a result of other mechanisms (e.g., edema

causing herniation). Of import, secondary injuries are particularly viable targets for

treatment, since some may be avoided with proper and timely intervention.

Finally, classifications have been developed based on clinical signs present at the

time of injury or soon thereafter in order to predict injury severity. These clinical

signs often include length of unconsciousness and post-traumatic amnesia (PTA),

neurological signs, and confusion and disorientation following injury (Ruff, Iverson,

Barth, Bush, & Broshek, 2009). Different professional organizations have used

combinations of these signs to develop criteria for classifying severity of braininjury (e.g., American Congress of Rehabilitation Medicine, 1993; Carroll, Cassidy,

Holm, Kraus, & Coronado, 2004), although there is some variability in the criteria

used across disciplines and sites.

One of the most commonly used clinical indicators is the Glasgow Coma Scale

score (GCS; Teasdale & Jennett, 1974), which reflects the depth of coma. The GCS

is a 15-point scale with severe injury defined as a score of 8 or less, moderate injury

as a score of 912, and a mild injury as reflected by scores of 13 or greater. GCS

scores have demonstrated usefulness in predicting a number of important outcomes

including the probability of cognitive recovery and the development of cerebralatrophy, among others (Cifu et al., 1997; Dikmen & Machamer, 1995; Ghosh et al.,

2009), although the GCS is not without limitations (Saatman et al., 2008). Another

clinical indicator is the length of PTA, or the time period following injury during

which continuous memory or the ability to store current events is impaired (Russell

& Smith, 1961; Wrightson & Gronwall, 1981). Russell first proposed the use of

PTA in 1932, and variations of this method continue to be used today to predict

clinical outcomes (e.g., Brown et al., 2010). Length of coma or unconsciousness has

been used in a similar manner, although criteria that identify when a person has

officially regained consciousness do vary. Some recommend a combination of theseand other indicators to improve prediction of outcomes (e.g., American Congress of

Rehabilitation Medicine, 1993; Carroll et al., 2004; Saatman et al., 2008; Sherer,

Struchen, Yablon, Wang, & Nick, 2008).

D.N. Allen et al.
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


5/29

99

While these methods have proven useful, they do have limitations, including that

some individuals who are initially classified as having severe injuries demonstrate

minimal long-term impairments. For example, when severity classification is made

using the GCS or other similar procedures, some children initially classified as hav-

ing severe TBI do not demonstrate significant neurocognitive or behavioral deficits

when examined after a period of recovery, and other factors including age at injury

and premorbid functioning may account for more variance in neurocognitive out-

comes (Fay et al., 2009; Lieh-Lai et al., 1992; Wells, Minnes, & Phillips, 2009).

Indeed, the GCS has been described as only a gross predictor of TBI severity and

functional outcome (Ghosh et al., 2009; Hackbarth et al., 2002; Hiekkanen, Kurki,

Brandstack, Kairisto, & Tenovuo, 2009). Saatman et al. (2008) also point out that

the GCS relies primarily on acute behavioral responses post-injury including best

eye, verbal, and motor response, but provides little information about the patho-

physiologic mechanisms underlying injury, which may provide additional insightson the nature and severity of the injury. They propose that an alternative, multidi-

mensional classification system that expands upon current qualitative observations

of behavior may be useful for future TBI clinical trials. In this regard, neuropsycho-

logical test results, particularly when used in combination with other indicators,

may afford a powerful method to help classify injury severity.

Neuropsychological Approaches

While acknowledged that neuropsychological testing cannot be used to classify

brain injury severity in the acute stages of moderate to severe injury for obvious

reasons, brief computerized neuropsychological batteries that are administered

shortly after mild TBI in athletes may show some promise in predicting protracted

recovery (e.g., Lau, Collins, & Lovell, 2012) and in this way may provide one

means for classifying severity of more mild injuries. Additionally, neuropsycho-

logical approaches to classification of severity of injury have been attempted and

progressed along a number of lines. Some advocate for the usefulness of impair-ment indexes that represent an average of impaired scores across a battery of tests

sensitive to brain injury (Reitan & Wolfson, 2009; Russell, Neuringer, & Goldstein,

1970). Such approaches have been shown to be predictive of the presence or absence

of brain injury, as well as the severity of injury. Others have utilized scores from an

individual measure or a limited number of measures, to screen for brain impairment

following injury (e.g., Reitan & Wolfson, 1995, 2004). In this approach, neuropsy-

chological tests are administered some time after injury, and results are used to

establish the severity of impairment in specific abilities that are ostensibly the result

of brain injury. For TBI, the feasibility of this approach has support from studiesthat demonstrate neuropsychological testing with brief test batteries can be con-

ducted within weeks following moderate to severe TBI (Boake et al., 2001; Sherer

et al., 2002), even before PTA has fully resolved in some patients (Kalmar et al.,

2008; Wilson et al., 1999). Kalmar and colleagues (2008)found that 32 % (n= 112)

http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


6/29

100

of their patients with moderate to severe TBI who were still experiencing PTA were

able to complete a brief battery of neuropsychological tests designed to take

6075 min to administer. Performance on this battery was predictive of important

outcomes such as functional independence and disability (Hanks et al., 2008).

Neuropsychological approaches may also provide additional information on the

pathophysiological changes that occur post-injury through the repeated measure-

ment of cognition and behavior over time (Goethe & Levin, 1986). These approaches

are therefore useful for broadly classifying injury severity, but do not typically cap-

ture the heterogeneity of neurocognitive deficits that results from TBI. In contrast,

cluster analysis may provide a unique approach to reflect both severity and heteroge-

neity of injury. While we do not provide an exhaustive review of the TBI cluster

analysis literature here, some representative studies are helpful to illustrate this point.

To date, cluster analytic studies have provided a number of unique insights into

TBI, and one organizing theme to these studies is that they address the issue of neu-ropsychological heterogeneity. Illustrative of this, a recent study by Allen et al.

(2010) investigated attention and memory heterogeneity in 150 children and adoles-

cents with TBI using the Test of Memory and Learning (TOMAL; Reynolds &

Bigler, 1994). The children with TBI were on average 11.7 years old (SD = 3.7),

52.1 % male, and 56.3 % Caucasian and were assessed 6.9 months (SD = 3.1) fol-

lowing injury. Clusters derived from this sample were compared to clusters derived

from 150 age- and sex-matched normal controls to determine whether differing

patterns of learning, memory, and attention/concentration would be evident among

the groups. Also, the TBI clusters were compared on a number of important clinical,cognitive, and behavioral variables, to determine whether cluster membership might

be associated with unique patterns of cognitive and behavioral disturbances. Results

of the cluster analyses for the TBI and control groups are presented in Fig. 5.2a, b.

As can be seen from the figure, cluster analyses indicated that a four-cluster solu-

tion was optimal for the control group (Fig. 5.2a), while a five-cluster solution was

optimal for the TBI group (Fig. 5.2b). Not only were there differences in the number

of clusters, the profiles of performance differed between the groups with the control

group clusters being primarily differentiated by level of performance, while the TBI

clusters were characterized by both level and pattern of performance differences.Differences were also present among the TBI clusters for neurocognitive, achieve-

ment, and behavioral variables not included in the cluster analysis, which provided

additional support for the validity of the cluster solution and its potential value in

predicting outcomes. Clusters characterized by impairment in verbal, nonverbal, or

global memory impairment generally had poorer neurocognitive and academic

achievement outcomes than clusters characterized by average memory performance

or attention deficits. In addition, the cluster characterized by global memory impair-

ment had increased parent- and teacher-reported behavioral problems. Thus, the

findings indicate that unique patterns of neurocognitive impairment are observed inchildren with TBI that distinguish them from non-brain-injured children, these

patterns of impairment are not accounted for by expected variation in test perfor-

mance observed in normal populations, and cluster membership is associated with

D.N. Allen et al.
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


7/29

101

patterns of outcome on some important clinical variables. These neurocognitive

profiles identified using cluster analysis may prove useful for identifying homo-

geneous subgroups of children with TBI that are differentiated by a number of

important clinical, cognitive, and behavioral variables associated with treatmentand outcomes.

The validity and usefulness of cluster solutions such as those identified by Allen

et al. (2010) is dependent not only on demonstrating between-cluster differences on

external variables but also on the degree to which they are generalizable from one

sample to another. Comparison of two studies of the Wechsler Intelligence Scale for

Children, Third Edition (WISC-III; Wechsler, 1991) conducted at different sites by

different investigators on separate study samples provides some support for the gen-

eralizability of neuropsychological clusters (Donders & Warschausky, 1997; Thaler

et al., 2010). Figure 5.3a, bprovide WISC-III profiles obtained in these two studies.Figure 5.3a presents results reported by Donders and Warschausky (1997) who

examined WISC-III performance of 153 children who sustained mild, moderate, or

severe closed head injuries. The sample was on average 11.8 years old, 52 % male,

and 87 % Caucasian and had a Full Scale IQ of 91.1. When the WISC-III scores

Fig. 5.2 Normal control and traumatic brain injury clusters on the Test of Memory and Learning.

Panel (a): normal controls. Panel (b): traumatic brain injury (From: Allen, D. N., Leany, B. D.,

Thaler, N. S., Cross, C., Sutton, G. P., & Mayfield, J. (2010). Memory and attention profiles in

pediatric traumatic brain injury.Archives of Clinical Neuropsychology, 25(7), 618633. Used with

permission. All rights reserved)

http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


8/29

102

were subjected to cluster analysis, four clusters were identified (see Fig. 5.3a).

Three of the clusters were characterized by either above average, average, or low

average index scores and in this way were differentiated primarily by level ofperformance differences. The fourth cluster exhibited low average scores on verbal

and attention indexes and impaired scores on the nonverbal and processing speed

indexes. No differences were present between the clusters on demographic vari-

ables, although significant differences were present on the GCS and neuroimaging

data, with the fourth cluster demonstrating more severe brain injury than the other

clusters. Thaler et al. (2010) also examined WISC-III clusters in 123 children with

TBI who were on average 11.6 years old, 58 % male, and had Full Scale IQ scores

of 82.4. The majority of these children sustained closed head injuries in the moder-

ate to severe range (Mean GCS = 7.1; median = 7). Cluster analysis of the WISC-IIIscores also identified four clusters that were similar in many respects to those identi-

fied by Donders and Warschausky (1997), as can be seen in Fig. 5.3b. Comparisons

between the clusters on behavioral ratings generally indicated that

the most severely impaired cluster typically exhibited the most severe behavioral

disturbances. The samples for these two studies were comparable in many respects.

Fig. 5.3 WISC-III cluster analysis results from Donders & Warschausky (1997) and Thaler et al.(2010). Panel (a): Donders et al. Panel (b): Thaler et al. (Panel (a) from: Donders, J., & Warschausky,

S. (1997). WISC-III factor index score patterns after traumatic head injury in children. Child

Neuropsychology, 3(1), 7178. Used with permission. All rights reserved. Panel (b) from: Thaler,

N. S., Bello, D. T., Randall, C., Goldstein, G., Mayfield, J., & Allen, D. N. (2010). IQ profiles are

associated with differences in behavioral and emotional functioning following pediatric traumatic

brain Injury.Archives of Clinical Neuropsychology, 25(8), 781790)

D.N. Allen et al.
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


9/29

103

Both studies identified average and low average clusters, as well as a more severely

impaired cluster with selective impairment on perceptual organization and process-

ing speed. The Donders and Warschausky (1997) sample contained more children

with mild TBI, which may account for the overall higher performance and the high

average cluster identified in that study, which was not identified by Thaler et al.

(2010).

These studies illustrate the potential usefulness of neuropsychological tests in

addressing the question of neuropsychological heterogeneity in TBI and demon-

strate that in addition to differences in pattern of performance, there are also level of

performance differences between neuropsychological clusters. These level of per-

formance differences range from mild to severe impairment and may correspond to

injuries also ranging from mild to severe. For practical purposes as it pertains to

classifying severity of brain injury, a brief measure that is quickly and easily admin-

istered and also sensitive to brain damage has certain advantages over more exten-sive assessments like the TOMAL and WISC. The TMT is one such measure and

the focus of the investigation described later in this chapter.

Trail Making Test

In the investigation, we examined TMT performance as an indicator of brain injury

severity approximately one year following injury in children who sustained a TBI.The TMT consists of Part A (TMT-A) and Part B (TMT-B). For adults, TMT-A

consists of a series of 25 numbered circles which the test subject is instructed to

connect in sequence by drawing a line from one circle to the next (i.e., start at 1,

draw a line to 2, then 3, and so on). TMT-B is similar to TMT-A, except that the 25

circles contain both letters and numbers. For TMT-B, the test subject is instructed to

connect the circles by alternating between the numerical and alphabetical sequences

(i.e., start at 1, and then draw a line to A, then 2, then B, and so on).

Performance is timed on both sections and the score is the amount of time (in sec-

onds) taken to complete each part. Errors are also recorded, although they are nottypically used when interpreting test performance.

Although well over 60 years old, the TMT continues to be one of the most fre-

quently administered neuropsychological tests in research and clinical practice

(Rabin, Barr, & Burton, 2005). The TMT was originally developed in 1938 as a test

of intelligence called the Test of Distributed Attention, and was then renamed the

Partingtons Pathways Test (Partington, 1949; Partington & Leiter, 1949; Watson,

1949). Later, it was included in the Army Individual Test of General Ability (U. S.

War Department, 1944) where it was called the TMT. The TMT was subsequently

incorporated into the HalsteadReitan Neuropsychological Battery (Reitan &Wolfson, 1992) but is also commonly used outside of this battery. In fact, a recent

survey of the members from the National Academy of Neuropsychology,

APA Division 40, and the International Neuropsychological Society found that the

TMT ranked third among the most frequently used instruments for clinical neuro-

psychological evaluation (Rabin et al., 2005). Consistent with its original design as

http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


10/29

104

a measure of intelligence, early studies indicated that the TMT was indeed signifi-

cantly correlated with tests of intelligence. For example, Partington and Leiter

(1949) found a correlation of 0.68 between the StandfordBinet 1937 Edition and

the Partingtons Pathways Test in a sample of 256 World War II veterans.

However, numerous studies thereafter established its sensitivity to brain dys-

function resulting from a wide variety of psychiatric and neurologic conditions,

including TBI in children and adults (Armitage, 1946; Barth et al., 1983; Levin,

Benton, & Grossman, 1982; Periez et al., 2007; Reitan, 1955, 1958, 1971; Reitan

& Wolfson, 1992). As recently reviewed by Allen, Thaler, Ringdahl, Barney, and

Mayfield (2012), the TMT in adults achieves overall correct classification rates of

approximately 84 % when normal controls are compared to mixed neurological

samples (Reitan, 1955, 1958). For older children, classification accuracies of 0.82

and 0.80 were found for TMT-A and TMT-B, respectively, when normal controls

were compared to children with mixed neurological disorders (Reitan & Herring,1985). Similarly, a TMT-B cut score of 37/38 achieved a correct classification rate

of 78.0 % when normal children were compared to those with brain damage (Reitan

& Wolfson, 2004). Comparable classification rates were also obtained when chil-

dren classified as either slow or normal learners were examined (Mittelmeier, Rossi,

& Berman, 1989). Some alternative versions of the original TMT, such as the

Comprehensive TMT (Reynolds, 2002), also show comparable classification rates

(Allen et al., 2012; Armstrong, Allen, Donohue, & Mayfield, 2008).

As a result, rather than being considered a test of intelligence as originally envi-

sioned by Partington, a TMT has gained popularity and widespread use because ofits sensitivity to brain injury and the recognition that successful performance

requires a number of abilities, including psychomotor speed, complex attention,

visual scanning, and mental flexibility. Differential associations between Parts A

and B of the TMT with other neuropsychological tests provide evidence that the two

parts are assessing somewhat different constructs. These correlational studies sug-

gest TMT-A is more reliant on perceptual abilities, visuoperceptual processing

speed, and motor speed, while TMT-B is more reliant on working memory, inhibi-

tion, and executive functions (Langenecker, Zubieta, Young, Akil, & Nielson, 2007;

Ros, Periez, & Muoz-Cspedes, 2004; Snchez-Cubillo et al., 2009; Thaleret al., 2012).

The popularity of the TMT is based in part on its brief administration time and

ease of administration, as well as its well-documented sensitivity to brain dysfunc-

tion. Accordingly, despite its simplicity, the TMT may also be useful for establish-

ing the severity of brain injury. This is particularly true when one considers that

motor and sensory deficits are common following TBI, as is slowed information

processing, although deficits in attention, concentration, and memory are also com-

mon (Babikian & Asarnow, 2009; Felmingham, Baguley, & Green, 2004). Also,

because neurocognitive deficits can present great challenges for rehabilitation andeducational placement (Kraemer & Blancher, 1997; Lowther & Mayfield, 2004)

and the TMT requires abilities that are often impaired by TBI, TMT performance is

expected to have some predictive power in this regard.

We present the results of a study that examined classification of brain injury

severity via TMT performance in a sample of children with TBI. Given its

D.N. Allen et al.
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


11/29

105

sensitivity to brain damage, the TMT was used as an indicator of brain injury sever-

ity, and cluster analysis was used as an empirical statistical approach to derive injury

severity subgroups. Following identification of an optimal cluster solution, compari-

sons were made among the clusters on important outcome domains including intel-

lectual, academic, and memory function to determine whether brain injury severity

classification derived from cluster analyzing the TMT scores would exhibit expected

associations with these outcomes. It was hypothesized that those classified as exhib-

iting greater severity of impairment would also demonstrate greater deficits in intel-

lect, poorer academic achievement, and greater impairment of memory abilities.

Comparisons were also made between the TMT clusters and classifications made at

the time of injury using the GCS. Given studies indicating inconsistent correspon-

dence between GCS scores at time of injury and later neurocognitive and functional

outcomes (Fay et al., 2009; Salorio et al., 2005), we did not anticipate that there

would be a high degree of consistency between the TMT and GCS classificationsand that the TMT classifications would be more strongly associated with intellec-

tual, academic, and memory functioning compared to the GCS classifications.

Method

Participants

Participants included 152 children and adolescents who had sustained a TBI that

were on average 12.9 years of age (SD = 2.8) with Full Scale IQ scores of 93.9

(SD = 14.8). They were 59.2 % male and 86.2 % right-hand dominant, and ethnicity

included 59.8 % Anglo/European, 20.6 % African American, 16.7 % Hispanic,

1.9 % Asian American, and 2.0 % others. They were assessed on average 13.8

months following injury. Of these children, 92.8 % had sustained closed head inju-

ries, with the most common causes of head injury including motor vehicle accidents

(50.7 %), pedestrian struck by a motor vehicle (21.1 %), four-wheeler accidents

(8.6 %), skiing accident (5.9 %), gunshot wound (3.3 %), bicycle accident (3.3 %),falls (1.3 %), and other causes (6.0 %). The GCS (Teasdale & Jennett, 1974) had

been completed for 97 of the children, either by first responders or after the children

were transported to the hospital, and indicated that overall they had sustained mod-

erate to severe TBI (median = 7.0; mean = 7.2, SD = 2.9).

Measures

TMT Parts A and B

The TMT assesses psychomotor speed, visual scanning, complex attention, and

mental flexibility. As previously discussed, the adult version of the TMT consists of

two parts, A and B, and both parts include 25 circles that are distributed across an

http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


12/29

106

8.5 by 11 in. sheet of paper. For Part A, the circles are numbered 1 to 25. The test

subject is given a pencil and instructed to draw a line as quickly as possible that con-

nects the 25 numbered circles in order. The older child version of the test, which was

used in the current study, is designed for children aged 915 years. It is identical to

the adult version except that it includes only the first 15 circles for Parts A and B.

Wechsler Intelligence Scales

Intelligence was assessed using several versions of the Wechsler Intelligence Scales

because the children were tested over a number of years and were of different ages.

Most were administered the WISC-III (n= 129), although other versions of the test

were also administered. Because these versions of the Wechsler scales share many

common subtests and these subtests were designed to assess similar abilities, datafrom the various versions were combined. Subtests that were selected for analysis

are strong indicators of their representative index scores, including Vocabulary (ver-

bal comprehension index), Block Design (perceptual organization/reasoning index),

Digit Span (working memory index), and Digit Symbol/Coding (processing speed

index). We also evaluated group differences for the Full Scale IQ.

The WoodcockJohnson Psycho-educational Battery Tests of Achievement

(WJ; Woodcock & Johnson, 1989; Woodcock, McGrew, & Mather, 2001)

Academic achievement was assessed with the WoodcockJohnson Psycho-

educational Battery Tests of Achievement Revised (Woodcock & Johnson, 1989) or

Third (Woodcock et al., 2001) Version. The Broad Reading and Broad Math cluster

scores were selected for analysis because these were completed by most partici-

pants and are generally consistent across the two versions of the test.

The Test of Memory and Learning (TOMAL; Reynolds & Bigler, 1994)

The TOMAL is a broad-based measure of memory and attention normed for children

between 5 and 19 years of age. Ten core subtests and four supplemental subtests

form indexes for verbal memory (VMI), nonverbal memory (NMI), delayed recall

(DRI), and attention/concentration (ACI), as well as an overall composite memory

(CMI). These indexes were compared across TMT clusters and GCS groups.

The Glasgow Coma Scale (Teasdale & Jennett, 1974)

The GCS is commonly used for assessing the severity of brain injury in TBI. The

GCS allows for ratings of three areas including Best Eye Response (score 14),

Best Verbal Response (score 15), and Best Motor Response (score 16). GCS

D.N. Allen et al.
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


13/29


14/29

108

analysis, but that represented outcomes in other important domains, including

intelligence, academic achievement, and memory. This was accomplished to deter-

mine the extent to which the TMT severity clusters identified using cluster analysis

were associated with other variables of interest. It was hypothesized that if the TMT

clusters represented a reliable method of classifying the sample, those with more

severe injuries would demonstrate greater impairment on these other indicators of

outcome. These comparisons were made using analysis of variance (ANOVA) or

multivariate ANOVA (MANOVA) in which cluster membership served as a

between-subjects variable and test scores as the dependent variables.

Finally, in order to examine the correspondence between severity classifications

produced by cluster analysis of the TMT and those produced by the GCS that was

completed at the time of injury, the sample was divided into three groups based on

GCS scores using accepted cutoffs. For mild, moderate, and severe brain injury

(Jennett & Teasdale, 1977; Teasdale & Jennett, 1974). Correspondence between theTMT and GCS classifications was accomplished using chi-squared analysis and

kappa coefficients, and comparisons were also made among the GCS groups on the

intellectual, academic, and memory variables with ANOVA or MANOVA to deter-

mine whether similar differences would be present for the GCS classifications as for

the TMT classifications.

Procedure

Participants in the TBI group were selected from a consecutive series of cases that

were referred for neuropsychological assessment to a pediatric specialty care hospi-

tal. Children from this series were included in this study if they had sustained a TBI,

completed the TMT as part of the neuropsychological evaluation, and had evidence

of structural brain damage based on appropriate neuroimaging, laboratory, and

other examinational findings including neurological evaluation. All tests were

administered according to standard procedures by a pediatric neuropsychologist or

by doctoral-level technicians under the supervision of the neuropsychologist.Children were evaluated 398 months following TBI (mean = 13.8; SD = 15.3). The

study was conducted in compliance with IRB regulations.

Results

Cluster Analysis of the TMT

Overall results for the measures used in the study are presented in Table 5.1. As can

be seen from the table, not all variables were available for all participants. When

data were missing, comparisons were made on the reduced number of cases.

D.N. Allen et al.
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


15/29

109

The three-, four-, and five-cluster solutions for the TMT are presented in Fig. 5.4.

Examination of the profiles based on TMT-A and TMT-B indicates that clusters in

the three-cluster solution were differentiated primarily by level of performance.

Discrepancies between the clusters were larger for TMT-B than for TMT-A,

although each cluster maintained its position with regard to overall performance.

The cluster that performed the best obtained scores that were in the average range,

and it was the largest cluster (n= 80). The second cluster was similar to the first on

TMT-A, but performed almost four SDs poorer on TMT-B (n= 42). The third cluster

was the smallest of the clusters (n= 30) and exhibited the worst performance over-

all, obtaining scores in the impaired range on both TMT-A and TMT-B. In the four-

cluster solution, the impaired cluster (C3) was divided into two clusters with the

fourth cluster (C4) exhibiting marked impairment on TMT-B and comparable or

somewhat better performance on TMT-A. However, this cluster consisted of only

Variable N Mean SD

TMT-A (sec) 152 31.0 14.3

TMT-B (sec) 152 70.2 42.8

Full Scale IQ 151 85.3 14.5

Vocabulary 151 7.7 3.1

Block Design 151 8.1 3.8

Digit Span 151 8.5 2.7

Coding 151 6.7 3.5

WJ Broad Reading 136 94.6 14.1

WJ Broad Math 136 96.8 16.7

TOMAL verbal 134 82.3 16.4

TOMAL nonverbal 134 84.0 16.1

TOMAL composite 134 82.7 15.1

TOMAL delayed 134 87.2 13.5

TOMAL attention 129 83.5 13.0

Note: Results are reported in standard scores except where

noted. TMT-ATrail Making Test Part A, TMT-BTrail Making

Test Part B, WJWoodcockJohnson

Table 5.1 Results

on primary variables

for all participants

Fig. 5.4 Three-, four-, and five-cluster solutions for the Trail Making Test Parts A and B



16/29

110

three participants. Finally, for the five-cluster solution, C4 was maintained and the

impaired cluster (C3) was divided again to form a fifth cluster (C5) that demon-

strated worse performance on TMT-A and TMT-B compared to C3. This cluster

consisted of only six participants.

Discriminant function analysis (DFA) results indicated that when the TMT

scores were used to predict cluster membership, there was a negligible difference in

the correct classification rates for the three-, four-, and five-cluster solutions, with

correct classification rates of 96.1 %, 95.4 %, and 97.4 %, respectively. The K-means

iterative partitioning method was then used to examine the stability of the three-,

four-, and five-cluster solutions. Cohens kappa indicated that the level of agreement

between the Wards and K-means results for the three-, four-, and five-cluster solu-

tions were 0.90, 0.92, and 0.96, respectively. Kappas above 0.80 are considered to

indicate excellent agreement (Landis & Koch, 1977), suggesting that all solutions

are stable as indicated by level of agreement between the Wards and K-meansmethods. Beales F tests were nonsignificant when comparing the three-cluster

solution to the four-cluster solution, F= 1.66,p= 0.19; the three-cluster to the five-

cluster solution, F= 1.58, p= 0.18; or the four-cluster to the five-cluster solution,

F= 1.29, p= 0.28. The Beales F test results suggest that compared to the three-

cluster solution, the four- and five-cluster solutions did not explain significantly

more variance. Finally, the additional clusters in the four- and five-cluster solutions

(C4 and C5) only accounted for 2.0 % and 3.9 % of the entire sample, respectively.

Considering these results, we turned our attention to the three-cluster solution.

While we considered the other clusters (particularly C5) theoretically interesting,given what appears to be somewhat unique impairment on TMT-A relative to TMT-

B, we decided that grouping these more severely impaired clusters into one severe

cluster (C3) would provide a better approach with regard to cluster stability, gener-

alizability to other samples, and power to make comparisons between the clusters

on external validity variables. Also, from a parsimony perspective, the three-cluster

solution appeared to be the best given the negligible differences between the three-,

four-, and five-cluster solutions from the DFA and K-means analyses, as well as

from the Beales Ftest results. A straightforward interpretation of the clusters based

on the TMT Part B scores would suggest that C1 could be considered a mild severitycluster, as scores for this cluster are generally in the normal range. Mild severity is

used to characterize this cluster rather than normal because of the fact that they

had sustained a TBI. A moderate cluster is also present (C2), as well as a more

severely impaired cluster (C3).

Comparisons Between the GCS Groups and TMT Clusters

One of the primary goals of the study was to compare the severity classifications of

the TBI sample that resulted from the GCS scores and the TMT clusters. This goal

was addressed in two ways, including a direct comparison of agreement between

the two different approaches to severity classification, as well as a comparison of the

GCS and TMT groups on the external validity variables.

D.N. Allen et al.
http://-/?-http://-/?-


17/29

111

Agreement Between the TMT and GCS Classifications

To form GCS severity groups, the GCS scores were used to classify the 97 partici-

pants with available GCS data based on established cutoffs. When classified in this

manner, 71.1 % were identified as severe, 23.7 % were identified as moderate,and 5.2 % were identified as mild. Given that GCS scores were missing for 55 of

the children, we compared those with GCS scores to those without GCS scores on

all of the demographic, clinical, and neuropsychological variables used in this study,

in order to determine if the groups were comparable. No significant differences

were present for any of the variables, with the exception of the Coding subtest of the

Wechsler scales,

F(1, 149) = 9.18,p< 0.01. The group without GCS scores was lower on this variable

than the group with GCS scores (means = 5.6 and 7.4, respectively), suggesting that

the group without GCS scores may have had more severe neurocognitive impair-ment. If that was the case, then the group might be most accurately characterized as

having sustained severe injuries. However, there were no significant differences on

TMT-A and TMT-B, which is arguably the most sensitive test to brain dysfunction

that we administered.

Table 5.2presents the severity classification agreement rates between the TMT

clusters and the GCS. Visual inspection of the table indicated low agreement

between the TMT and GCS groups, which was confirmed using Cohens kappa,

which suggested very poor agreement between the two methods (kappa = 0.02,

p= 0.98). For example, cases in the mild TMT cluster (C1) were distributed acrossall GCS levels, while cases classified as severe by the GCS were distributed across

all of the TMT clusters, with most (n= 37) falling in the mild TMT cluster.

In addition to the low correspondence between the two approaches, examination

of the mean TMT and GCS scores for the classification groups also demonstrates

expected absence of differences. Table 5.3presents descriptive statistics for each

classification approach as well as results of between-group analyses (ANOVA). For

the TMT clusters, there were large between-group differences for TMT Part A and

B, with similar results for the GCS scores of the GCS groups. These differences

were expected because the TMT and GCS scores were used to develop the TMTclusters and GCS groups, respectively. However, the analyses further indicated that

the TMT clusters did not significantly differ with regard to GCS scores, while the

GCS clusters did not differ on TMT variables. The absence of differences provides

additional evidence that the TMT and GCS approaches are yielding quite different

classification results.

Table 5.2 Cross-tabulation of severity classification for the Trail Making Test (TMT) clusters and

Glasgow Coma Scale (CGS) severity groups

TMT clusters

Mild Moderate Severe

GCS group Mild 2 (2.1%) 2 (2.1%) 1 (1.0%)

Moderate 12 (12.4%) 7 (7.2%) 4 (4.1%)

Severe 37 (38.1%) 19 (19.6%) 13 (13.4%)

http://-/?-http://-/?-http://-/?-http://-/?-


18/29

112

TMT and GCS Classification Comparisons on Demographic

and Clinical Variables

Next, the TMT clusters and GCS groups were compared across demographic, clini-

cal, intellectual, achievement, and memory variables. These variables are consid-

ered external validity variables in this analysis since they were not included in the

cluster analysis, and differences between groups on these variables would provide

support for the validity of the two classification approaches. Comparisons on demo-graphic and clinical variables are presented in Table 5.4. None of the differences

between the groups were significant, suggesting that TMT cluster membership and

GCS group membership is not substantially influenced by these variables.

TMT and GCS Classification Comparisons on IQ Variables

Descriptive statistics for IQ variables are presented in Table 5.5for the TMT clus-

ters and GCS groups. For the TMT clusters, significant differences emerged for allthe Wechsler subtests as well as the Full Scale IQ. However, no differences were

present for the GCS groups on the Wechsler subtests, although there was a signifi-

cant difference for Full Scale IQ. Figure 5.5presents the Wechsler subtest profiles

for the GCS groups (Fig. 5.5a) and the TMT clusters (Fig. 5.5b). As seen in the

figure, there was some overlap across the four subtests among GCS groups, particu-

larly with the Digit Span subtest in which all three groups converged in level of

performance. The mild group otherwise appeared to perform better than the moder-

ate and severe groups, which exhibited little distinction from each other with the

exception of the Digit Symbol subtest where clearer distinction between the threegroups was apparent. Post hoc analyses indicated that for the Full Scale IQ, the

severe and moderate groups performed significantly worse than the mild group, but

did not differ from each other. Regarding the TMT clusters, the overlap was reduced

and particularly differentiated the severe cluster from the mild and moderate

Table 5.3 Differences between Trail Making Test (TMT) clusters and Glasgow Coma Scale

(CGS) groups on classification variables


Mean SD Mean SD Mean SD F p

TMT clusters

GCS 7.0 2.8 7.6 2.9 7.0 3.0 0.41 0.67

TMT-A 23.7 7.8 35.1 11.5 44.9 18.5 39.56


19/29

113

Table 5.4 Demographic and clinical variables for the Glasgow Coma Scale (GCS) groups and

Trail Making Test (TMT) clusters

Variable Mild Moderate Severe X2 p

TMT clusters

Gender (% male) 57.5 61.9 60.0 0.23 0.89

Ethnicity (%) 12.39 0.13

Caucasian 64.6 50.0 63.6

African American 12.5 31.3 4.5

Hispanic 20.8 18.8 22.7

Other 2.1 0 9.0

Closed HI (%) 94.9 95.2 86.7 2.73 0.26

GCS groups

Gender (% male) 40.0 65.2 56.5 1.21 0.55

Ethnicity (%) 6.01 0.65

Caucasian 100.0 50.0 63.8

African American 0.0 16.7 21.3

Hispanic 0.0 33.3 10.6

Other 0.0 0.0 4.2

Closed HI (%) 80.0 100.0 92.8 3.30 0.19

Mean SD Mean SD Mean SD F p

TMT clusters

Age (years) 12.6 2.3 13.4 3.3 12.9 3.1 1.29 0.28

Months since injury 14.6 16.1 13.3 12.7 12.5 17.0 0.24 0.79

GCS groupsAge (years) 14.7 2.3 11.9 2.7 12.9 2.7 2.52 0.09

Months since injury 12.0 7.5 9.5 7.0 13.5 14.3 0.85 0.43

Note: GCSGlasgow Coma Scale, TMTTrail Making Test,HIhead injury

Table 5.5 IQ variables for the Glasgow Coma Scale (GCS) groups and Trail Making Test (TMT)

clusters

Mild Moderate SevereVariable Mean SD Mean SD Mean SD F p

p2

TMT clusters

Vocabulary 8.3 3.2 7.5 2.8 6.2 2.8 5.63


20/29

114

clusters. Inspection of the severe cluster indicates that children in this cluster had

general cognitive impairment with even greater deficits on the Block Design and

Coding subtests. This pattern of performance resembles a pattern that was identifiedin studies of children with TBI that examined the WISC-III. In those studies there

was a subgroup of severely impaired children who exhibited relative deficits on the

Perceptual Organization and Processing Speed indexes (Donders & Warschausky,

1997; Thaler et al., 2010; see Fig. 5.3). Post hoc comparisons indicated that for all

of the subtests, the mild cluster (C1) significantly differed from the severe cluster

(C3), and for the Block Design and Coding subtests, as well as the Full Scale IQ, the

moderate cluster (C2) also significantly differed from the severe cluster. There were

no significant differences between the mild and moderate clusters on any of the

Wechsler subtests.

TMT and GCS Classification Comparisons on Achievement Variables

Result for the WoodcockJohnson achievement variables are presented in Table 5.6

and Fig. 5.6. For the TMT clusters, significant differences were found for both the

Broad Reading and Broad Math, while the GCS groups significantly differed only on

the Broad Math score. Plots of achievement variables are present on Fig. 5.6. Again

children with mild injuries were distinct from children with moderate and severe inju-

ries when grouped into GCS scores, while children with severe injuries were distinct

from children with mild and moderate injuries when grouped into TMT clusters. Post

hoc analyses indicated that for the GCS clusters, there were no significant differences

among the groups on the Broad Math score, although there was a trend difference

Fig. 5.5 Comparison of the GCS and Trail Making Test severity groups on selected subtests from

the Wechsler Intelligence Scales. Panel (a): GCS groups. Panel (b): TMT clusters. Note: VO

Vocabulary,BDBlock Design,DSDigit Span, CDCoding

D.N. Allen et al.
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


21/29

115

between the mild and severe group (p= 0.059) for the Broad Reading Score. For the

TMT clusters, the mild and moderate groups significantly differed from the severe

group for both Reading and Math, and there was a trend toward significant differences

between the mild and moderate group on the Math score (p= 0.052).

TMT and GCS Classification Comparisons on Memory Variables

For the TOMAL indexes, descriptive statistics and between-group comparisons arepresented in Table 5.7. Again, for the TMT clusters significant differences emerged

for all of the memory variables, while for the GCS groups, no statistically signifi-

cant differences were present although there was a trend toward significance for the

Delayed Recall Index (p= 0.09). The pattern of performance for the TOMAL

Table 5.6 Achievement test variables for the Glasgow Coma Scale (GCS) groups and Trail

Making Test (TMT) clusters


Variable Mean SD Mean SD Mean SD F p p

2

TMT clusters

WJ Broad Reading 97.1 14.1 95.2 9.4 87.4 16.9 5.16


22/29

116

indexes is presented in Fig. 5.7. Similar to the IQ and achievement variables, the

TMT clusters overall appeared to better identify children with severe injuries from

those with mild and moderate injuries, while the GCS groups appeared to better

identify children with mild impairment from those with moderate and severe impair-

ment. Post hoc analyses indicated that the significant differences for the TMT clus-

ters on all TOMAL indexes were accounted for by significant differences between

the severe group, compared to the mild and moderate groups, who did not differ

from each other.

Fig. 5.7 Comparison of the GCS and Trail Making Test severity groups on selected subtests from

the Test of Memory and Learning. Panel (a): GCS groups. Panel (b): TMT clusters

Table 5.7 Memory variables for the Glasgow Coma Scale (GCS) groups and Trail Making Test

(TMT) clusters


Variable Mean SD Mean SD Mean SD F p p

2

TMT clusters

TOMAL VMI 85.7 16.0 86.2 11.7 67.0 14.9 16.68


23/29

117

Discussion

Results of the current study provide support for the usefulness of the TMT as a

method to classify severity of TBI in children and adolescents. The TMT clusterscorrespond in a general way with mild, moderate, and severe classifications,

although the best performing cluster obtained scores that were in the average range.

However, in this context they are considered to be in the mild severity group rather

than normal because they did sustain a brain injury, which was well documented

and, in some cases, classified as severe based on GCS scores. Whether these were

children who had high levels of premorbid function for whom normal performance

still represented a decline from baseline functioning or might have actually experi-

enced substantial recovery of function to near premorbid levels could not be directly

evaluated here.Inspection of the figures indicates that the TMT clusters appear to differenti-

ate the severe cluster from the mild and moderate clusters, which is consistent

with the statistical analyses of cluster differences on the IQ, achievement, and

memory variables. TMT clusters differed on all intellectual, academic, and

memory variables, with the severe cluster consistently performing below the

moderate and mild clusters, and these differences appeared unrelated to demo-

graphic or clinical variables such as age, time since injury, and GCS scores. Our

results provide strong evidence that children who perform in the severe range

on both the TMT-A and TMT-B following TBI exhibit cognitive and academic

impairment across a number of domains that have implications for real-world

outcomes. Children who perform in the moderate range had less distinction

from those in the mild range, although certain IQ domains and Full Scale IQ

did differentiate between the moderate and severe groups. Additionally, the

moderate group consistently performed below the mild group on many of the

outcomes that were examined. The absence of significant differences between

the mild and moderate clusters may have been accounted for by the relatively

severe nature of injury sustained by the current sample, with few mild injury

cases included. If mild cases were included, then the distinction between the

mild and moderate group is expected to be larger, or another cluster may have

appeared composed primarily of cases with mild initial injuries who performed

average or above on the TMT.

It is also worth noting that while there were minimal statistical differences among

the GCS groups, when visually plotted they appear to differentiate the mild group

from the moderate and severe groups. Severe scores as measured by the GCS had

little correspondence with TMT clusters, and, as might be expected, there was mini-

mal correspondence between the GCS groups with neuropsychological variables,

particularly between those initially classified with moderate and severe injuries.

Only Full Scale IQ differentiated among the GCS groups, which like the GCS maybe considered a more general indicator of overall functioning. There was clear sepa-

ration between the mild GCS group and the moderate and severe groups, although

these differences did not achieve statistical significance, probably because of the



24/29

118

small number of cases in the mild GCS group. The TMT clusters could therefore be

interpreted as a more refined classification system particularly with regard to neuro-

psychological and academic outcomes for the moderate and severe clusters and so

may have greater relevance for predicting persisting deficits across neurocognitive

and academic domains. This is not totally unexpected, as the TMT was adminis-

tered at the same time as the tests of intelligence, achievement, and memory, so

higher correspondence would be anticipated between the TMT clusters and these

measures than would be expected for the GCS groups and neuropsychological mea-

sures. Nonetheless, the results clearly demonstrate a lack of correspondence between

severity ratings made at the time of injury using the GCS and later classification

using the TMT.

Since the patients were tested with the TMT well after the acute phase of injury,

the low correspondence between GCS scores and TMT may be largely accounted

for by this factor since the GCS is typically administered during the acute phase ofinjury, often by first responders at the scene of the accident, or by the ER staff as

was the case for the children in this study. As such, it is a measure of acute injury,

while the TMT is generally administered during a follow-up exam some weeks after

injury. However, the finding is consistent with longitudinal studies of neuropsycho-

logical recovery following TBI that have demonstrated that initial GCS classifica-

tions are limited with regard to predicting post acute recovery of neurocognitive

abilities. For example, Fay et al. (2009) followed a sample of children for four years

who were classified according to GCS scores as having moderate or severe TBI.

They found that while the moderate group had fewer children with no neuropsycho-logical deficit at follow-up (81 %) compared to the severe group (67 %), a similar

number demonstrated persistent deficits (14 % and 17 %, respectively). Also, dura-

tion of unconsciousness but not lowest GCS score predicted neuropsychological

outcomes. Direct comparisons between the moderate and severe injury groups were

not made, so it is unclear whether the differences in number of children with

moderate and severe injuries who demonstrated neuropsychological deficits were

significant, but studies such as this suggest a lack of correspondence between

neuropsychological and GCS scores.

Thus, some children who are classified as severely impaired at the time ofinjury based on the GCS will attain normal levels of neuropsychological function-

ing at some point in the future (Fay et al., 2009; Salorio et al., 2005). The TMT may

be a better predictor of longitudinal functioning as it is typically assessed at a time

when patients have been medically stabilized and cognitive functioning is unlikely

to abruptly change. In contrast, the GCS is more appropriate for classification during

the acute stage of injury, as it loses its effectiveness as patients regain conscious-

ness. In this way, the TMT and GCS may be viewed as complimentary approaches.

Although both TMT-A and TMT-B significantly differed among the three TMT

clusters, inspection of the figure clearly reveals that the TMT Part B had a greatersensitivity to brain damage, which is consistent with other studies comparing

TMT-A and TMT-B scores (Demery, Larson, Dixit, Bauer, & Perlstein, 2010; Stuss

et al., 2001). As suggested by Reitan and Wolfson (2004), TMT-B is particularly

useful as a screen for brain dysfunction. Our mild cluster had a mean TMT Part B

D.N. Allen et al.
http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-


25/29

119

score of 40.8 (SD = 11.1), our moderate cluster had a mean score of 75.5 (SD = 10.5),

and our severe cluster a mean score of 141.2 (SD = 37.6). These scores may repre-

sent approximate cutoff points for classifying TBI severity with the TMT Part B,

although results need to be replicated with additional outcome variables before

conclusively establishing their validity. In our sample the moderate and severe TMT

clusters identified only those children with residual deficits (given time since injury

was 13.8 months on average). In this regard, these data provide evidence that

clusters are useful in predicting other important domains of functioning (intellect,

achievement, and memory) and so may prove useful in documenting recovery of func-

tion and for predicting more general outcomes throughout the recovery process.

The current study has a number of limitations. First, generalizability of results is

limited by the nature of the TBI sample, which was composed of children and

adolescents referred to a pediatric specialty care hospital for neuropsychological

evaluation following TBI. They were included in this study if they had been admin-istered the TMT as part of that evaluation, and the large majority had sustained

moderate to severe TBI. Thus, it is not clear whether the TMT clusters would have

been more distinct, particularly the mild and moderate clusters, if children with

mild TBI were included in the study, although one would suspect that this would be

the case. Similarly, inclusion of cases with mild injuries may have resulted in addi-

tional clusters that were not identified here. Also, while this sample was large

enough for cluster analysis particularly for the TMT variables, the number of cases

with GCS scores was more limited, making direct comparisons between the GCS

groups and TMT clusters difficult. Cross-validation of the current results is there-fore necessary in other TBI samples.

As a related concern, the mild GCS group only had a few participants. Therefore,

some variables that approached significance, such as the WISC Digit Symbol

Coding and the WoodcockJohnson Broad Reading cluster, might have been dif-

ferentiated among the GCS groups with larger and balanced sample sizes. However,

the TMT cluster effect sizes were consistently larger than those of the GCS groups,

suggesting that even with equal sample sizes, the TMT clusters may be more effec-

tive in classifying severity of neurocognitive impairments. In addition, analysis of

only those cases with GCS scores did indicate that the general pattern noted for theTMT clusters remained, particularly with regard to the external validity variables,

where there was clear distinction of the severe cluster from the mild and moderate

clusters. Finally, children in our sample were evaluated with the TMT an average of

13.8 months after they were injured, so it may be that better separation between the

groups and higher predictive power on outcome variables might have been achieved

if the evaluation occurred closer to the time of injury.

Future studies should investigate whether or not the TMT demonstrates similar

usefulness in classifying severity of brain injury and dysfunction in other clinical

populations, as well as adults. Studies should also further establish appropriate cut-off scores for the TMT-A and TMT-B clusters that might be useful in demarcating

severity of injury. Along with additional neurocognitive variables, these clusters

should be compared across behavioral variables and other outcome measures that

are relevant for TBI rehabilitation. It may also be informative to examine the utility



26/29

120

of the TMT when it is administered closer to the acute phase of injury, as it may

better have better predictive validity and provide a better comparison to the GCS.

Finally, the role of environmental factors on cluster membership was not examined,

which may help explain differences in TMT performance following injury and be

useful in understanding the recovery process (Taylor et al., 2008). The current find-

ings do provide evidence that the TMT can serve as a brief classification instrument,

though continued research is necessary to replicate these findings with other sam-

ples with differing demographic and clinical characteristics.

References

Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Beverly Hills, CA: Sage.Allen, D. N., Leany, B. D., Thaler, N. S., Cross, C., Sutton, G. P., & Mayfield, J. (2010). Memory

and attention profiles in pediatric traumatic brain injury.Archives of Clinical Neuropsychology,

25(7), 618633.

Allen, D. N., Thaler, N. S., Ringdahl, E. N., Barney, S., & Mayfield, J. (2012). Comprehensive trail

making test performance in children and adolescents with traumatic brain injury. Psychological

Assessment, 24(3), 556564.

American Congress of Rehabilitation Medicine; Mild Traumatic Brain Injury Committee. (1993).

Definition of mild traumatic brain injury. The Journal of Head Trauma Rehabilitation, 8(3),

8687.

Armitage, S. G. (1946). An analysis of certain psychological tests used for the evaluation of brain

injury. Psychological Monographs, 60(1), 148. Whole No. 277.Armstrong, C., Allen, D. N., Donohue, B., & Mayfield, J. (2008). Sensitivity of the comprehensive

trail making test to traumatic brain injury in adolescents.Archives of Clinical Neuropsychology,

23, 351358.

Babikian, T., & Asarnow, R. (2009). Neurocognitive outcomes and recovery after pediatric TBI:

Meta-analytic review of the literature. Neuropsychology, 23(3), 283296. doi:10.1037/

a0015268.

Barth, J. T., Macciocchi, S. N., Giordani, B., Rimel, R., Jane, J. A., & Boll, T. J. (1983).

Neuropsychological sequelae of minor head injury.Neurosurgery, 13, 529533.

Boake, C., Millis, S. R., High, W. M. Jr., et al. (2001). Using early neuropsychologic testing to

predict long-term productivity outcome from traumatic brain injury. Archives of Physical

Medicine and Rehabilitation, 82, 761768.Brown, A. W., Malec, J. F., Mandrekar, J., Diehl, N. N., Dikmen, S. S., Sherer, M., et al. (2010).

Predictive utility of weekly post-traumatic amnesia assessments after brain injury: A multicen-

tre analysis.Brain Injury, 24(3), 472478.

Carroll, L. J., Cassidy, J. D., Holm, L., Kraus, J., & Coronado, V. G. (2004). Methodological issues

and research recommendations for mild traumatic brain injury: the WHO Collaborating centre

task force on mild traumatic brain injury. Journal of Rehabilitation Medicine, 43(Suppl),

113125.

Cifu, D. X., Keyser-Marcus, L., Lopez, E., Wehman, P., Kreutzer, J. S., Englander, J., et al. (1997).

Acute predictors of successful return to work 1 year after traumatic brain injury: A multicenter

analysis.Archives of Physical Medicine and Rehabilitation, 78, 125131.

Curtiss, G., Vanderploeg, R. D., Spencer, J., & Salazar, A. M. (2001). Patterns of verbal learningand memory in traumatic brain injury.Journal of the International Neuropsychological Society,

7(5), 574585. doi:10.1017/S1355617701755051.

Demery, J. A., Larson, M. J., Dixit, N. K., Bauer, R. M., & Perlstein, W. M. (2010). Operating

characteristics of executive functioning tests following traumatic brain injury. The Clinical

Neuropsychologist, 24(8), 12921308. doi:10.1080/13854046.2010.528452.

D.N. Allen et al.
http://-/?-http://dx.doi.org/10.1037/a0015268http://dx.doi.org/10.1037/a0015268http://dx.doi.org/10.1037/a0015268http://dx.doi.org/10.1017/S1355617701755051http://dx.doi.org/10.1017/S1355617701755051http://dx.doi.org/10.1080/13854046.2010.528452http://dx.doi.org/10.1080/13854046.2010.528452http://dx.doi.org/10.1080/13854046.2010.528452http://dx.doi.org/10.1017/S1355617701755051http://dx.doi.org/10.1037/a0015268http://dx.doi.org/10.1037/a0015268http://-/?-


27/29

121

Dikmen, S., & Machamer, J. (1995). Neurobehavioral outcomes and their determinants. The

Journal of Head Trauma Rehabilitation, 10, 7486.

Donders, J., & Warschausky, S. (1997). WISC-III factor index score patterns after traumatic head

injury in children. Child Neuropsychology, 3(1), 7178.

Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster analysis(4th ed.). London: Arnold.

Faul, M., Xu, L., Wald, M. M., & Coronado, V. G. (2010). Traumatic brain injury in the United

States: Emergency department visits, hospitalizations and deaths 20022006. Atlanta, GA:

Centers for Disease Control and Prevention, National Center for Injury Prevention and Control.

Fay, T. B., Yeates, K., Wade, S. L., Drotar, D., Stancin, T., & Taylor, H. (2009). Predicting longi-

tudinal patterns of functional deficits in children with traumatic brain injury.Neuropsychology,

23(3), 271282. doi:10.1037/a0014936.

Felmingham, K. L., Baguley, I. J., & Green, A. M. (2004). Effects of diffuse axonal injury on speed

of information processing following severe traumatic brain injury. Neuropsychology, 18(3),

564571. doi:10.1037/0894-4105.18.3.564.

Gennarelli, T. A., & Thibault, L. E. (1985). Biomechanics of head injury. In R. H. Wilkins & S. S.

Rengachary (Eds.),Neurosurgery(pp. 15311536). New York: McGraw-Hill.Ghosh, A., Wilde, E. A., Hunter, J. V., Bigler, E. D., Chu, Z. L., Li, X. Q., et al. (2009). The relation

between Glasgow Coma Scale score and later cerebral atrophy in paediatric traumatic brain

injury.Brain Injury, 23(3), 228233.

Goethe, K. E., & Levin, H. S. (1986). Neuropsychological consequences of head injury in children.

In G. Goldstein & R. E. Tarter (Eds.),Advances in clinical neuropsychology(Vol. 3, pp. 213

242). New York: Plenum Press.

Hackbarth, R. M., Rzeszutko, K. M., Sturm, G., Donders, J., Kuldanek, A. S., & Sanfilippo, D. J.

(2002). Survival and functional outcome in pediatric traumatic brain injury: A retrospective

review and analysis of predictive factors. Critical Care Medicine, 30(7), 16301635.

Hanks, R. A., Millis, S. R., Ricker, J. H., Giacino, J. T., Nakese-Richardson, R., Frol, A. B., et al.

(2008). The predictive validity of a brief inpatient neuropsychologic battery for persons withtraumatic brain injury.Archives of Physical Medicine and Rehabilitation, 89, 950957.

Hiekkanen, H., Kurki, T., Brandstack, N., Kairisto, V., & Tenovuo, O. (2009). Association of

injury severity, MRI-results and ApoE genotype with 1-year outcome in mainly mild TBI: A

preliminary study.Brain Injury, 23(5), 396402. doi:10.1080/02699050902926259.

Jennett, B., & Teasdale, G. (1977). Aspects of coma after severe head injury. The Lancet, 1(8017),

878881.

Kalmar, K., Novack, T. A., Nakase-Richardson, R., Sherer, M., Frol, A. B., Gordon, W. A., et al.

(2008). Feasibility of a brief neuropsychologic test battery during acute inpatient rehabilitation

after traumatic brain injury. Archives of Physical Medicine and Rehabilitation, 89, 942949,

doi:10.1016/j.apmr.2008.01.008 .

Kraemer, B. R., & Blancher, J. (1997). An overview of educationally relevant effects, assessment,and school reentry. In A. Glang, G. H. S. Singer, & B. Todis (Eds.), Students with acquired

brain injury: The school response(pp. 331). Baltimore, MD: Paul H. Brookes.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data.

Biometrics, 33(1), 159174.

Langenecker, S. A., Zubieta, J., Young, E. A., Akil, H., & Nielson, K. A. (2007). A task to manipu-

late attentional load, set-shifting, and inhibitory control: Convergent validity and test-retest

reliability of the parametric Go/No-Go test. Journal of Clinical and Experimental

Neuropsychology, 29(8), 842853. doi:10.1080/13803390601147611.

Lau, B. C., Collins, M. W., & Lovell, M. R. (2012). Cutoff scores in neurocognitive testing and

symptom clusters that predict protracted recovery from concussions in high school athletes.

Neurosurgery, 70(2), 371379, doi:10.1227/NEU.0b013e31823150f0.Levin, H. S., Benton, A. L., & Grossman, R. G. (1982).Neurobehavioral consequences of closed

head injury. New York: Oxford University Press.

Lieh-Lai, M. W., Theodorou, A. A., Samaik, A. P., Meert, K. L., Moylan, P. M., & Canady, A. L.

(1992). Limitations of the Glasgow Coma Scale in predicting outcome in children with

traumatic brain injury. The Journal of Pediatrics, 120(2), 195199.

http://dx.doi.org/10.1037/a0014936http://dx.doi.org/10.1037/a0014936http://dx.doi.org/10.1037/0894-4105.18.3.564http://dx.doi.org/10.1037/0894-4105.18.3.564http://dx.doi.org/10.1080/02699050902926259http://dx.doi.org/10.1080/02699050902926259http://dx.doi.org/10.1016/j.apmr.2008.01.008http://dx.doi.org/10.1016/j.apmr.2008.01.008http://dx.doi.org/10.1080/13803390601147611http://dx.doi.org/10.1080/13803390601147611http://dx.doi.org/10.1227/NEU.0b013e31823150f0http://dx.doi.org/10.1227/NEU.0b013e31823150f0http://dx.doi.org/10.1227/NEU.0b013e31823150f0http://dx.doi.org/10.1080/13803390601147611http://dx.doi.org/10.1016/j.apmr.2008.01.008http://dx.doi.org/10.1080/02699050902926259http://dx.doi.org/10.1037/0894-4105.18.3.564http://dx.doi.org/10.1037/a0014936


28/29

122

Lowther, J. L., & Mayfield, J. (2004). Memory functioning in children with traumatic brain injuries:

A TOMAL validity study.Archives of Clinical Neuropsychology, 19(1), 89104. doi:10.1016/

S0887-6177(02)00222-6.

Mittelmeier, C., Rossi, J. S., & Berman, A. (1989). Discriminative ability of the trail making test

in young children.International Journal of Clinical Neuropsychology, 11(4), 163166.

Morris, R., Blashfield, R., & Satz, P. (1981). Neuropsychology and cluster analysis: Potentials and

problems.Journal of Clinical Neuropsychology, 3(1), 7999. doi:10.1080/01688638108403115.

Mottram, L., & Donders, J. (2006). Cluster subtypes on the california verbal learning test-

childrens version after pediatric traumatic brain injury. Developmental Neuropsychology,

30(3), 865883. doi:10.1207/s15326942dn3003_6.

Partington, J. E. (1949). Detailed instructions for administering Partingtons pathways test. The

Psychological Service Center Journal, 1, 4648.

Partington, J. E., & Leiter, R. G. (1949). Partingtons pathways test. The Psychological Service

Center Journal, 1, 1120.

Periez, J. A., Ros-Lago, M., Rodrguez-Snchez, J. M., Adrover-Roig, D., Snchez-Cubillo, I.,

Crespo-Facorro, B., et al. (2007). Trail making test in traumatic brain injury, schizophrenia,and normal ageing: Sample comparisons and normative data. Archives of Clinical

Neuropsychology, 22, 433447.

Rabin, L., Barr, W., & Burton, L. (2005). Assessment practices of clinical neuropsychologists in

the United States and Canada: A survey of INS, NAN, and APA Division 40 members.Archives

of Clinical Neuropsychology, 20(1), 3365.

Reitan, R. M. (1955). The relation of the trail making test to organic brain damage. Journal of

Consulting Psychology, 19, 393394.

Reitan, R. M. (1958). Validity of the trail making test as an indicator of organic brain damage.

Perceptual and Motor Skills, 8, 271276.

Reitan, R. M. (1971). Trail making test results for normal and brain-damaged children. Perceptual

and Motor Skills, 33, 575581.Reitan, R. M., & Herring, S. (1985). A short screening device for identification of cerebral

dysfunction in children.Journal of Clinical Psychology, 41(5), 643650.

Reitan, R. M., & Wolfson, D. (1992). The Halstead-Reitan neuropsychological test battery:

Theory and clinical interpretation(2nd ed.). Tucson, AZ: Neuropsychology Press.

Reitan, R. M., & Wolfson, D. (1995). The category test and the trail making test as measures of

frontal lobe functions. The Clinical Neuropsychologist, 9, 5056.

Reitan, R. M., & Wolfson, D. (2004). The trail making test as an initial screening procedure for

neuropsychological impairment in older children. Archives of Clinical Neuropsychology, 19,

281288.

Reitan, R. M., & Wolfson, D. (2009). The Halstead-Reitan neuropsychological test battery for

adultsTheoretical, methodological, and validational bases. In I. Grant, K. M. Adams, I.Grant, & K. M. Adams (Eds.),Neuropsychological assessment of neuropsychiatric and neuro-

medical disorders(3rd ed., pp. 324). New York: Oxford University Press.

Reynolds, C. R. (2002). Comprehensive trail making test (CTMT). Austin, TX: PRO-ED, Inc.

Reynolds, C. R., & Bigler, E. D. (1994). Test of memory and learning: Examiners manual. Austin,

TX: Pro-Ed.

Rivara FP, Koepsell TD, Wang J, Temkin N, Dorsch A, Vavilala MS, Durbin D, Jaffe KM.

Incidence of disability among children 12 months after traumatic brain injury. Am J Public

Health. 2012, 102(11):2074-9. doi: 10.2105/AJPH.2012.300696.

Ros, M., Periez, J. A., & Muoz-Cspedes, J. M. (2004). Attentional control and slowness of

information processing after severe traumatic brain injury.Brain Injury, 18(3), 257272. doi:1

0.1080/02699050310001617442.Ruff, R. M., Iverson, G. L., Barth, J. T., Bush, S. S., & Broshek, D. K. (2009). Recommendations

for diagnosing a mild traumatic brain injury: A National Academy of neuropsychology educa-

tion paper.Archives of Clinical Neuropsychology, 24, 18.

Russell, E. W., Neuringer, C., & Goldstein, G. (1970).Assessment of brain damage: A neuropsy-

chological key approach. Oxford: Wiley-Interscience.

D.N. Allen et al.
http://dx.doi.org/10.1016/S0887-6177(02)00222-6http://dx.doi.org/10.1016/S0887-6177(02)00222-6http://dx.doi.org/10.1016/S0887-6177(02)00222-6http://dx.doi.org/10.1080/01688638108403115http://dx.doi.org/10.1080/01688638108403115http://dx.doi.org/10.1207/s15326942dn3003_6http://dx.doi.org/10.1207/s15326942dn3003_6http://dx.doi.org/10.1080/02699050310001617442http://dx.doi.org/10.1080/02699050310001617442http://dx.doi.org/10.1080/02699050310001617442http://dx.doi.org/10.1080/02699050310001617442http://dx.doi.org/10.1080/02699050310001617442http://dx.doi.org/10.1207/s15326942dn3003_6http://dx.doi.org/10.1080/01688638108403115http://dx.doi.org/10.1016/S0887-6177(02)00222-6http://dx.doi.org/10.1016/S0887-6177(02)00222-6


29/29

123

Russell, W. R., & Smith, A. (1961). PTA in closed head injury.Archives of Neurology, 5, 1629.

Saatman, K. E., Duhaime, A. C., Bullock, R., Maas, A. I., Valadka, A., Manley, G. T., et al. (2008).

Classification of traumatic brain injury for targeted therapies.Journal of Neurotrauma, 25(7),

719738.

Salorio, C. F., Slomine, B. S., Grados, M. A., Vasa, R. A., Christensen, J. R., & Gerring, J. P.

(2005). Neuroanatomic correlates of CVLT-C performance following pediatric traumatic brain

injury.Journal of the International Neuropsychological Society, 11(6), 686696. doi:10.1017/

S1355617705050885.

Snchez-Cubillo, I., Periez, J. A., Adrover-Roig, D., Rodrguez-Snchez, J. M., Ros-Lago, M.,

Tirapu, J., et al. (2009). Construct validity of the trail making test: role of task-switching,

working memory, inhibition/interference control, and visuomotor abilities. Journal of the

International Neuropsychological Society, 15(3), 438450.

Sherer, M., Sander, A. M., Nick, T. G., High, W. M. Jr., Malec, J. F., & Rosenthal, M. (2002). Early

cognitive status and productivity outcome after traumatic brain injury: findings from the TBI

model systems.Archives of Physical Medicine and Rehabilitation, 83, 183192.

Sherer, M. M., Struchen, M. A., Yablon, S. A., Wang, Y. Y., & Nick, T. G. (2008). Comparison ofindices of traumatic brain injury severity: Glasgow Coma Scale, length of coma and post-

traumatic amnesia. Journal of Neurology, Neurosurgery & Psychiatry, 79(6), 678685.

doi:10.1136/jnnp. 2006.111187.

Stuss, D. T., Bisschop, S. M., Alexander, M. P., Levine, B., Katz, D., et al. (2001). The trail making

test: A study in focal lesion patients. Psychological Assessment, 13, 230239.

Suskauer, S. J., & Huisman, T. M. (2009). Neuroimaging in pediatric traumatic brain injury:

Current and future predictors of functional outcome. Developmental Disabilities Research

Reviews, 15(2), 117123. doi:10.1002/ddrr.62.

Taylor, H. G., Swartwout, M. D., Yeates, K. O., Walz, N. C., Stancin, T., & Wade, S. L. (2008).

Traumatic brain injury in young children: Postacute effects on cognitive and school readiness

skills.Journal of the International Neuropsychological Society, 14(5), 73445.Teasdale, G., & Jennett, B. (1974). Assessment of coma and impaired consciousness: A practical

scale.

cluster tbi chapter 2013

Documents