Neurometabolite Concentrations in Gray and White Matter in Mild Traumatic Brain Injury: A 1H–Magnetic Resonance Spectroscopy Study

Article (PDF Available)inJournal of neurotrauma 26(10):1635-43 · May 2009with37 Reads
DOI: 10.1089/neu.2009-0896 · Source: PubMed
Abstract
Single-voxel proton magnetic resonance imaging ((1)H-MRS) and proton MR spectroscopic imaging ((1)H-MRSI) were used to compare brain metabolite levels in semi-acute mild traumatic brain injury (mTBI) patients (n = 10) and matched healthy controls (n = 9). The (1)H-MRS voxel was positioned in the splenium, a region known to be susceptible to axonal injury in TBI, and a single (1)H-MRSI slice was positioned above the lateral ventricles. To increase sensitivity to the glutamate (Glu) and the combined glutamate-glutamine (Glx) signal, an inter-pulse echo time shown to emphasize the major Glu signals was used along with an analysis method that reduces partial volume errors by using water as a concentration standard. Our preliminary findings indicate significantly lower levels of gray matter Glx and higher levels of white matter creatine-phosphocreatine (Cr) in mTBI subjects relative to healthy controls. Furthermore, Cr levels were predictive of executive function and emotional distress in the combined groups. These results suggest that perturbations in Cr, a critical component of the brain's energy metabolism, and Glu, the brain's major neurotransmitter, may occur following mTBI. Moreover, the different pattern of results for gray and white matter suggests tissue-specific metabolic responses to mTBI.

Figures

Original Article
Neurometabolite Concentrations in Gray and White Matter
in Mild Traumatic Brain Injury: An
1
H–Magnetic
Resonance Spectroscopy Study
Charles Gasparovic,
1,2,4
Ronald Yeo,
1,4
Maggie Mannell,
1
Josef Ling,
1
Robert Elgie,
3
John Phillips,
1,2
David Doezema,
3
and Andrew R. Mayer
1,2
Abstract
Single-voxel proton magnetic resonance imaging (
1
H-MRS) and proton MR spectroscopic imaging (
1
H-MRSI)
were used to compare brain metabolite levels in semi-acute mild traumatic brain injury (mTBI) patients (n ¼ 10)
and matched healthy controls (n ¼ 9). The
1
H-MRS voxel was posit ioned in the splenium, a region known to be
susceptible to axonal injury in TBI, and a single
1
H-MRSI slice was positioned above the lateral ventricles. To
increase sensitivity to the glutamate (Glu) and the combined glutamate-glutamine (Glx) signal, an inter-pulse
echo time shown to emphasize the major Glu signals was used along with an analysis method that reduces
partial volume errors by using water as a concentration standard. Our preliminary findings indicate significantly
lower levels of gray matter Glx and higher levels of white matter creatine-phosphocreatine (Cr) in mTBI subjects
relative to healthy controls. Furthermore, Cr levels were predictive of executive function and emotional distress
in the combined groups. These results suggest that perturbations in Cr, a critical component of the brain’s energy
metabolism, and Glu, the brain’s major neurotransmitter, may occur following mTBI. Moreover, the different
pattern of results for gray and white matter suggests tissue-specific metabolic responses to mTBI.
Key words: cognitive; creatine; glutamate; mild traumatic brain injury; spectroscopy
Introduction
M
ild traumatic brain injury (mTBI) is by far the most
prevalent form of brain injury, accounting for over 70%
of hospital visits related to head injury, with many cases likely
to go unreported (Arciniegas et al., 2005; Jennett, 1998). Al-
though most patients recover fully, mTBI produces acute
cognitive and emotional symptoms by mechanisms that are
still not understood. Furthermore, the effects of repeated
mTBI may be cumulative or enhance susceptibility to injury
upon a subsequent insult (DeFord et al., 2002; Matser et al.,
1998; Vagnozzi et al., 2008; Vagnozzi et al., 2007). These issues
raise unique questions in the medical management of mTBI,
such as when to advise otherwise healthy-appearing patients
to return to work, to the playing field, or, as has become more
relevant in recent years, the battlefield (Hoge et al., 2008).
Moreover, as standard clinical imaging modalities produce
negative results in the majority of mTBI cases, there is a great
need for the development of clinical measures that are more
sensitive to the subtle alterations in brain morphology,
physiology, and function that may underlie mTBI (Belanger
et al., 2007). Among the more promising of these measures is
proton magnetic resonance spectroscopy (
1
H-MRS), which
measures the levels of several key metabolites in the brain
(Belanger et al., 2007; Brooks et al., 2001; Shutter et al., 2006).
To date, the majority of mTBI studies using
1
H-MRS
have been aimed at detecting group differences in N-
acetylaspartate (NAA) or in the ratio of the total NAA signal
to the signal from other metabolites in the
1
H-MRS spectrum,
such as choline (Cho) or creatine-phosphocreatine (Cr) (Ba-
bikian et al., 2006; Cimatti, 2006; Cohen et al., 2007; Go-
vindaraju et al., 2004; Kirov et al., 2007; Son et al., 2000;
Tavazzi et al., 2007; Vagnozzi et al., 2005; Vagnozzi et al.,
2008; Vagnozzi et al., 2007). Most often the combined NAA
and N-acetylaspartylglutamate (NAAG) signal is measured,
since the latter is relatively small and difficult to resolve at
clinical magnetic field strengths. As the NAA, Cho, and Cr
methyl ‘singlet’ peaks are the most prominent signals in the
1
The Mind Research Network, Albuquerque, New Mexico.
2
Neurology Department and
3
Department of Emergency Medicine, University of New Mexico School of Medicine, Albuquerque,
New Mexico.
4
Department of Psychology, University of New Mexico, Albuquerque, New Mexico.
JOURNAL OF NEUROTRAUMA 26:1635–1643 (October 2009)
ª Mary Ann Liebert, Inc.
DOI: 10.1089=neu.2009.0896
1635
1
H-MRS spectrum, their detection is relatively reliable. Typi-
cal acquisition parameters (i.e., long echo times) emphasize
these peaks while discriminating against other metabolite and
macromolecule signals in the otherwise crowded and over-
lapping
1
H-MRS spectrum. Lower levels of NAA have
generally been interpreted to reflect either neuronal loss,
metabolic dysfunction, or myelin repair, owing respectively,
to its predominant location in neurons, its synthesis in neu-
ronal mitochondria, and its possible role as an acetyl group
donor in lipid synthesis (Moffett et al., 2007). Additionally,
NAA has been proposed to act as an osmolyte tightly coupled
to synaptic transmission (Baslow et al., 2007). Changes in the
Cho signal, primarily from membrane lipid metabolites
phosphorylcholine and glycerol phosphorylcholine, have
been interpreted to reflect membrane damage and=or repair
in TBI (Garnett et al., 2000a; Garnett et al., 2000b; Shutter et al.,
2004; Yeo et al., 2006). Creatine and phosphocreatine, in
equilibrium with adenosine tri- and diphosphate (ATP and
ADP), maintain the cell’s ATP levels. Since their individual
1
H-MRS signals are generally not resolvable at clinical scanner
field strengths, their combined signal (Cr) is often assumed to
be constant, regardless of cellular energy status. However,
this assumption has been challenged by recent studies
showing the Cr signal to be higher or lower relative to healthy
controls in different disease states (Hattingen et al., 2008;
Inglese et al., 2003; Munoz Maniega et al., 2008).
Owing to the increased challenges in reliably measuring
more complex ‘multiplet’
1
H-MRS signals, far fewer studies
have been directed toward measuring the levels of glutamate
(Glu), glutamine (Gln), or g-aminobutyric acid (GABA) after
TBI, even though these neurotransmitters or neurotransmitter
metabolites (i.e., Gln) are prime candidates for disruption
after TBI, and their importance for normal brain function is
much better understood than that of NAA. Glu, the brain’s
major neurotransmitter, is taken up in the synaptic cleft by
astrocytes and converted to Gln, which is then shuttled back
to the presynaptic neuron and converted to Glu. However,
there are a number of alternative metabolic pathways for both
Glu and Gln, including energy metabolism and the produc-
tion of glutathione. The flux through any particular pathway
depends on local demands for maintaining homeostasis
during neuronal firing (McKenna, 2007). Several studies using
microdialysis probes have detected a disruption in this ho-
meostasis, specifically, elevated Glu levels that persist for
several hours following severe TBI in humans (Alves et al.,
2005; Koura et al., 1998; Vespa et al., 1998; Zauner et al., 1996),
and in animal models of moderate to severe TBI (Di et al.,
1999; Globus et al., 1995; Hartley et al., 2008; Nilsson et al.,
1990; Palmer et al., 1993). In studies using
1
H-MRS, an ele-
vation in the sum of the glutamate and glutamine signals
(Glx) and Cho was related to poorer outcome on the Glasgow
Outcome Scale from 6 months to 1 year later in severely in-
jured adults (Shutter et al., 2004). The
1
H-MRS Glx signal has
also been shown to be elevated in moderate to severe pediatric
TBI (Ashwal et al., 2004), and Glx levels measured within 2–10
days of TBI have been found to correlate negatively with
cognitive function in children 1–4 years after injury (Babikian
et al., 2006). To our knowledge, a study examining Glu or Glx
changes in a well-characterized cohort of mTBI patients has
not been performed to date.
In the present study, we used both single-voxel
1
H-MRS
and proton MR spectroscopic imaging (
1
H-MRSI) to compare
brain metabolite levels, including those of Glu, in a group of
subjects within 3 weeks of mTBI to metabolite levels in a
group of normal healthy control subjects matched for age,
gender, and education. The single
1
H-MRS voxel was posi-
tioned in the splenium to observe any metabolic changes in a
region known to be susceptible to axonal injury upon TBI
(Thierry et al., 2004), and a single
1
H-MRSI slice was posi-
tioned above the lateral ventricles in tissue comprised of both
gray matter (GM) and white matter (WM).
Methods
Participants
Ten (6 female, 4 male) patients with mTBI and nine healthy
uninjured (5 female, 4 male) gender-, age-, and education-
matched controls were recruited for the current study (see
Table 1 for demographic data). One mTBI patient did not
complete the neuropsychological assessment, but did receive
the full MRS protocol. All mTBI patients experienced a closed
head injury resulting in an alteration in mental status. They
were evaluated clinically (mean assessment at 10.89 days
post-injury; range 4–19 days), and with the imaging protocol
(mean assessment at 10.67 days post-injury; range 3–19 days)
as soon after injury as was practical, given scheduling issues.
The etiologies of the brain injuries included two motor vehicle
accidents, six falls, one assault, and one patient who was
struck by a falling object. Inclusion criteria for mTBI patients
were based on the American Congress of Rehabilitation
Medicine recommendations, and included a Glasgow Coma
Scale score of 13–15 (at presentation in the emergency de-
partment), and an alteration in mental status (e.g., confusion,
loss of consciousness, or post-traumatic amnesia) at the time
of injury. Loss of consciousness (if present) was limited to
30 min in duration, and post-traumatic amnesia was limited
to a 24-h period. Mild TBI participants and controls were
excluded if there was a previous history of neurological dis-
ease, major psychiatric disturbance, additional closed-head
injuries with more than 5 min of loss of consciousness,
learning disorder, ADHD, or history of substance or alcohol
abuse. At the time of assessment, two of the mTBI subjects
were being prescribed medications for pain. Informed consent
was obtained from subjects according to institutional review
board guidelines of the University of New Mexico.
Image and
1
H-MRSI acquisition
MRI and
1
H-MRSI experiments were performed on a
Seimens 3T scanner. T
1
-weighted images were acquired in a
Table 1. Demographic Data for the Mild TBI
and Healthy Control Participants
Mild TBI Healthy controls
Mean SD Range Mean SD Range
Age 29.00 9.71 21–49 27.56 9.07 21–49
Education 12.60 2.68 6–16 13.67 2.12 9–16
WTAR 47.33 9.17 33–59 51.44 9.06 40–63
Handedness
quotient
89.49 29.55 18–100 62.87 48.87 –43–100
The Wechsler Test of Adult Reading (WTAR) provides an estimate
of premorbid intellectual functioning.
1636 GASPAROVIC ET AL.
sagittal plane perpendicular to the inter-hemispheric fissure
with a 3-D multi-echo MPRAGE sequence [echo time
(TE) ¼ 1.64 msec, repetition time (TR) ¼ 2.53 sec, 78 flip angle,
number of excitations (NEX) ¼ 1, slice thickness ¼ 1 mm, FOV
(field of view) ¼ 256256 mm, matrix ¼ 256256, voxel size ¼
111mm
3
]. T
2
-weighted images [TE ¼ 77.0 msec, TR ¼
1.55 sec, NEX ¼ 1, slice thickness ¼ 1.5 mm, FOV ¼ 220 mm,
matrix ¼ 192192, voxel size ¼ 1.151.151.5 mm
3
]wereac-
quired with a 3-D variable flip-angle turbo-spin-echo sequence
in an oblique axial plane perpendicular to the inter-hemispheric
fissure and parallel to an axis defined by the inferior borders of
the splenium and the genu in the T
1
-weighted image.
1
H-MRSI was performed with a phase-encoded version of
a point-resolved spectroscopy sequence (PRESS) with and
without water presaturation (TE ¼ 40 msec, TR ¼ 1500, slice
thickness ¼ 15 mm, FOV ¼ 220220 mm, circular k-space
sampling [radius ¼ 24], total scan time ¼ 582 sec). A TE of
40 msec was chosen to enhance Glu detection (Mullins et al.,
2008). The nominal voxel size was 6.256.2515 mm
3
after
zero-filling in k-space to 3232 samples. The
1
H-MRSI vol-
ume of interest (VOI) was selected with strong saturation
bands to reduce chemical shift artifacts and was prescribed
with the TSE image to lie 1 cm above the lateral ventricles
and parallel to the AC-PC axis. This location was chosen to
include the superior aspects of the anterior cingulate gyrus,
the inferior aspects of the medial frontal gyrus, and the su-
perior longitudinal fasciculus. To further minimize the che-
mical shift artifact, the transmitter was set to the frequency of
the NAA methyl peak during the acquisition of the metab-
olite spectra, and to the frequency of the water peak dur-
ing the acquisition of the unsuppressed water spectra.
Additionally, the outermost rows and columns of the VOI
were excluded from analysis. Figure 1 (panels A, C, and D)
shows the location of a representative
1
H-MRSI VOI and
spectrum.
Single-voxel
1
H-MRS data from a 111-cm voxel posi-
tioned within the splenium (Fig. 1B) was obtained with a
PRESS sequence with and without water suppression
(TR=TE ¼ 1500=40 msec, 192 averages with water suppres-
sion, 16 averages without water suppression). The transmitter
frequency was set to 2.3 ppm for the acquisition of the me-
tabolite spectrum, and to 4.7 ppm (water resonance) for the
acquisition of the water spectrum, to minimize the chemical
shift artifact.
1
H-MRS data processing
After zero-filling to 3232 points in k-space, applying a
Hamming filter with a 50% window width, and 2-D spatial
Fourier transformation (FT), the time domain
1
H-MRSI data
were analyzed using the LCModel (Provencher, 1993) using
tissue water as a concentration reference. We used the
LCModel output statistic on the Cramer-Rao lower bounds of
the fit to the peak of interest as a criterion to exclude data of
poor quality from the final analysis. If this statistic was >20%
for the major peaks of interest, the spectrum was excluded.
Since the major signals from NAAG are not resolved from
those of NAA at 3T, and moreover are expected to be much
less intense than those of NAA (Pouwels and Frahm, 1997),
we report the combined NAA and NAAG concentration in
this work (which we refer to as NAA). The results from the
LCModel were corrected for CSF, WM, and GM content
(partial volume effects) as previously reported (Gasparovic
et al., 2006). Briefly, tissue segmentation was performed on
the T
1
-weighted image using SPM5 (Ashburner and Friston,
2005). The individual GM, WM, and CSF maps were then
registered to the spectroscopic region of interest and con-
volved with the theoretical
1
H-MRSI point spread function
(PSF) to smooth the maps to the resolution of the
1
H-MRSI
data. Before the convolution step, the map pixels outside of
the
1
H-MRSI VOI are set to zero, since protons in this region
are assumed to contribute negligibly to the signal intensities
within the VOI. To convolve the maps with the
1
H-MRSI PSF,
an inverse FT was applied to the map. This k-space matrix is
multiplied by a matrix that is the inverse FT of the
1
H-MRSI
PSF. The latter matrix is constructed as a Hamming function
FIG. 1. Spectroscopic regions of interest locations and example spectrum. (A) Location of supraventricular axial proton
magnetic resonance spectroscopy imaging (
1
H-MRSI) slice in sagittal plane. (B) Location of 1-cm
3
single-proton magnetic
resonance imaging (
1
H-MRS) voxel in splenium (red box). (C) Location of
1
H-MRSI slice in axial plane (region of interest is
within the white box). (D) Spectrum from the cingulate region (from voxel in purple box in C) (Cr, creatine-phosphocreatine;
Cho, choline; ml, myoinositol; Glx, glutamate-glutamine; NAA, N-acetylaspartate).
NEUROMETABOLISM IN MILD TBI 1637
(used as the spatial filter for the
1
H-MRSI data) with radius 24,
normalized to a peak amplitude of 1, and centered in a matrix
of zeros with the resolution of the map (i.e., 192
2
or 256
2
). This
product is Fourier transformed, resulting in a map effectively
smoothed to the resolution of the
1
H-MRSI data. To obtain the
fractional GM, WM, and CSF in each
1
H-MRSI voxel, the pixel
values of the smoothed maps are summed and normalized
over the volume of each
1
H-MRSI voxel for each tissue class.
The resolution of the water density and relaxation-corrected
water density maps are similarly reduced to that of the
1
H-
MRSI data by summing and normalizing the data over the
volume spanned by each
1
H-MRSI pixel. Finally, the LCMo-
del results are corrected for partial volume effects in each
voxel according to the equation:
[M] ¼
[M]
LCM
· (f
GM
· R
H2O
GM
þ f
WM
· R
H2O
WM
þ f
CSF
· R
H2O
CSF
)
(1 f
CSF
) · R
M
[1]
where [M]
LCM
is the concentration in mmol per kg of MR-
visible water (mmolal) as determined by the LCmodel, based
on using tissue water as a concentration reference; f
GM
,f
WM
,
and f
CSF
are the water density fractions for GM, WM, and CSF,
respectively; and the R
H2O
terms are the relaxation attenua-
tion factors for the water signal in each tissue class, based
on reported values for T
1
and T
2
and the equation R
H2O_y
¼
exp[-TE=T
2_H2O_y
](1-exp[-TR=T
1_H2O_y
]), where T
1_H2O_y
and
T
2_H2O_y
are the T
1
and T
2
relaxation times of water in com-
partment y, TE is the sequence echo time, and TR is the rep-
etition time. Similarly, R
M
is the relaxation attenuation factor
for the metabolite signal, assumed to be similar in both GM
and WM.
The fractional water densities appearing in Eq. [1] are re-
lated to the tissue volume fractions obtained by tissue seg-
mentation by taking into account the relative water densities
(WD) in each volume fraction:
f
x
¼
f
x
vol
· WD
x
f
GM
vol
· WD
GM
þ f
WM
vol
· WD
WM
þ f
CSF
vol
· WD
CSF
[2]
where the various terms refer to the volume fractions and
associated water densities of each tissue or CSF (i.e., x ¼ GM,
WM, or CSF). In this study, we used a CSF T
1
value of 4 sec
based on a recent report (Rooney et al., 2007) and a CSF T
2
estimate of 2.47 sec based on a previous measurement at our
site. Otherwise, the previously reported T
1
,T
2
, and WD val-
ues used were as follows: GM: T
1
¼ 1.304 sec, T
2
¼ 0.093 sec
(Vymazal et al., 1999), WD ¼ 0.78 (Kreis et al., 1993); WM:
T
1
¼ 0.660 sec, T
2
¼ 0.073 sec (Vymazal et al., 1999); WD ¼ 0.65
(Kreis et al., 1993); CSF: WD ¼ 0.97 (Ashburner and Friston,
2005; Kreis et al., 1993). Estimates of metabolite T
1
and T
2
values at 3T were drawn from Mlynarik and associates
(Mlynarik et al., 2001). The Gln T
1
and T
2
values were as-
sumed to be equal to the Glu values.
After estimating metabolite concentrations in each spec-
troscopic voxel by the methods above, the concentrations in
pure GM and WM for each subject were estimated by linear
regression of the metabolite concentration against the nor-
malized GM fraction (f
GM_vol
=(f
GM_vol
þ f
WM_vol
) in each
voxel. Extrapolation of the regression line to 0 or 1 provides an
estimate of the metabolite content of pure WM or pure GM,
respectively. All of the steps outlined above were performed
with a program developed in MATLAB
7.2.
Single-voxel
1
H-MRS data were also corrected for partial
volume effects as above, with the exception of reducing the
resolution of the segmentation maps or performing linear
regression. Owing to the small size of the voxel, and conse-
quently low signal-to-noise ratio of the data, Cramer-Rao
lower bounds of < 20% were achieved consistently only for
NAA, Cho, and Cr in this data set.
Neuropsychological assessment
All participants were administered a battery of neu-
ropsychological tests selected to provide a comprehensive
assessment of attention, working memory, processing speed,
executive function, memory, and emotional status. Within
each cognitive domain the relevant test scores were converted
to T-scores (mean ¼ 50, SD ¼ 10) using published age-specific
norms, and then averaged to provide an overall composite
score (see Table 2 for the specific tests in each composite). The
Wechsler Test of Adult Reading (WTAR) provided an esti-
mate of overall premorbid cognitive functioning. Handedness
was assessed with the Edinburgh Handedness Inventory
(Oldfield, 1971).
Results
There were no significant differences between the two
groups ( p > 0.10) on any of the major demographic variables
or for hand preference (Table 1). Additionally, independent
samples t-tests of each major neuropsychological domain
score revealed no significant differences between the healthy
controls and mTBI groups (Table 3). However, examination of
group means suggested that the mTBI patients had lower
scores on the executive domain and higher scores on the
emotional distress domain scores. Moreover, the effect sizes
for both of these variables fell in the ‘large’ range (Cohen,
1992). For the mTBI group scores on normative measures
comprising the emotional distress domain, the Beck Depres-
sion Inventory and the State-Trait Anxiety Scale, were all
within the normal range, suggesting that overt psychopa-
thology is not a compelling explanation for the neuroimaging
findings described below. As evidenced in Table 3, the poorer
performance by mTBI patients on tests in the executive
function domain was approximately two-thirds of a standard
deviation below normal, and is consistent with their common
complaints of mild cognitive problems in daily life.
Our stringent methodological approach for neurometabo-
lite measurements (Table 4 and Fig. 2) produced data with
coefficients of variation of 3% in GM NAA and 5% in WM
NAA in the healthy control subjects. Because the Glx peak
includes Glu, and the concentrations of these two peaks are
highly correlated (r ¼ .78, p < 0.001 in GM, and r ¼ 0.93,
p < 0.001 in WM), we used Glx for our principal analyses to
minimize the number of statistical comparisons. Specifically,
group differences in neurometabolite concentrations were
evaluated with a series of MANOVAs for NAA, Cho, Cr, and
Glx. For the NAA, Cho, and Cr MANOVAs, the different
values from GM, WM, and the splenium served as the de-
pendent variables, with group membership being the inde-
pendent variable. The splenium data were too noisy to resolve
the Glx peak. Therefore only the GM and WM values were
used as dependent measures for the Glx MANOVA.
1638 GASPAROVIC ET AL.
The multivariate effect of group was not significant for
MANOVAs examining NAA ( p > 0.10) or Cho ( p > 0.10). In
contrast, the multivariate effect of group was significant for
the Cr analysis (F
1,15
¼ 5.21, p < 0.05), and a strong trend was
observed for Glx (F
1,17
¼ 3.61, p ¼ 0.051). Univariate analyses
indicated that Cr concentrations in both WM (F
1,15
¼ 6.28,
p < 0.05) and the splenium (F
1,15
¼ 7.31, p < 0.05) were signif-
icantly greater in the mTBI group compared to the controls.
Univariate tests also indicated that GM Glx concentrations
were significantly lower in the mTBI group (F
1,17
¼ 6.13,
p < 0.05). However, a non-significant trend indicated that the
direction of this effect was reversed for WM Glx, as patients
showed greater concentrations (F
1,17
¼ 3.42; p ¼ 0.08). Pro-
vided with these findings, exploratory t-tests were performed
to investigate whether similar results would be obtained for
Glu. Consistent with the Glx results, the mTBI group dis-
played non-significant trends for lower GM Glu (t
17
¼ 2.04,
p ¼ 0.058), and higher WM Glu (t
17
¼ 1.89, p ¼ 0.076).
Our next set of analyses evaluated whether Cr and Glx
concentrations would predict the executive and emotional
distress domain scores (i.e., domains with large effect sizes)
collapsed across both groups of subjects. Four multiple re-
gression analyses were conducted. The three Cr measures
(GM, WM, and splenium) were used to predict executive
function in one analysis and emotional distress in another.
Both Cr analyses were significant. For executive function, the
total model (F
3,12
¼ 3.72, p < 0.05) was significant, with Cr
from the splenium being retained as the only significant in-
dividual predictor (standardized beta ¼.71, p < 0.01). For
emotional distress, the total model was significant (F
3,12
¼
3.92, p < 0.05), with Cr from the white matter being retained
as the only significant individual predictor (standardized
beta ¼ .62, p < 0.05). Identical analyses were conducted with
the two Glx variables; however, none of these approached
statistical significance.
Finally, Vagnozzi and colleagues (2008) recently reported
recovery of NAA=Cr 30 days after injury in a small sample of
individuals who had suffered a concussion. Though we had
limited power to examine correlations within our TBI group,
and limited range in terms of chronicity, we found that overall
gray plus white matter NAA=Cr was positively correlated
with days post-injury at a trend level (r ¼ 0.60, p ¼ 0.07).
Discussion
This study of mTBI revealed perturbations in Cr, Glx, and
Glu, but not NAA and Cho. Since lower white matter NAA
and higher Cho have been interpreted to reflect axonal injury
in past studies of TBI, an interpretation of the current findings
consistent with this view would be that alterations in cerebral
energy and neurotransmitter metabolism are a more funda-
mental abnormality in mTBI than alterations in axonal
integrity. Specifically, compared to well-matched, healthy con-
trols, mTBI patients showed elevated Cr in supraventricular
Table 2. Neuropsychological Tests Administered and Organization of Specific Scores
into Cognitive Composites
Domain Test Score
Attention Trail-Making Test A Total time
Paced Auditory Serial Addition Test T-scores from 4 blocks
Stroop Interference Test (Color Word) Total read
Digit Span Forward Total points
Memory California Verbal Learning Test II Total, trials 1–5
Short-delay free recall
Long-delay free recall
Long-delay recognition
Working memory WAIS III: Letter-Number Sequence Scaled score
WAIS III: Arithmetic Scaled score
WAIS III: Digits Backward Total points
Processing speed Grooved Pegboard Dominant hand time
Non-dominant hand time
WAIS III: Digit Symbol Scaled score
Executive function Wisconsin Card-Sorting Test Total errors
Perseverative errors
Trail-Making Test B Total time
Controlled Oral Word Association Test Total number of words
Emotional distress Beck Depression Inventory II Total score
State-Trait Anxiety Scale Total score
WAIS III, Wechsler Adult Intelligence Scale III.
Table 3. Differences in Neuropsychological
Composite t-Scores for the Mild TBI
and Healthy Control Participants
Mild TBI Healthy controls
Composite Mean SD Mean SD
p Value=effect
size
Attention 50.89 7.01 51.44 6.29 .86=0.08
Memory 51.89 8.64 49.67 5.43 .52=0.31
Working memory 48.44 7.72 49.78 9.47 .75=0.15
Processing speed 48.67 7.71 48.00 5.22 .83=0.10
Executive 43.78 6.83 48.22 6.92 .19=0.65
Emotion
a
48.33 6.36 43.33 7.19 .14=0.74
a
Higher t-scores on the emotion index indicate greater emotional
distress.
Significance levels determined by independent samples t-tests.
NEUROMETABOLISM IN MILD TBI 1639
WM and in the splenium, as well as reduced GM Glx and Glu.
In contrast, non-significant differences were observed between
the groups for both clinical and cognitive measures, consistent
with the general clinical observation of mild and transient
cognitive deficits following mTBI (Belanger et al., 2005; Bigler,
2008). However, large effect sizes were observed for functional
deficits in executive skills and emotional distress in the mTBI
patients. Hence, the current results provide confirmatory evi-
dence that the cognitive deficits seen during the semi-acute
stage (from days to 3 weeks post-injury) of mTBI are mild in
nature, and these results provide preliminary evidence sug-
gesting that
1
H-MRS may be more sensitive than neu-
ropsychological findings in predicting group differences.
Moreover, Cr concentrations were associated with both exec-
utive functioning and emotional disturbances, suggesting that
alterations in this metabolite may serve as a bio-marker for the
mild cognitive and emotional disturbances that characterize
mTBI in the semi-acute stage.
The majority of previous studies of moderately- to severe-
ly-injured patients have reported that absolute concentrations
of NAA are lower and those of Cho are higher relative to
control subjects (e.g., Friedman et al., 1998), findings that have
been interpreted as reflecting diffuse axonal injury. Similar
findings have been reported by two other MRS studies of
Table 4. Magnetic Resonance Spectroscopy Variables in Gray Matter (GM), White Matter (WM),
and Splenium for Mild TBI Patients and Controls
Mild TBI Healthy controls
Metabolite Location Mean SD Range Mean SD Range p Value
NAA GM 20.84 1.27 19.63–23.61 20.96 .84 19.54–22.14 0.78
WM 17.17 .72 15.84–18.28 17.22 1.61 14.33–19.60 0.64
Splenium 17.21 3.41 8.20–20.15 18.48 2.65 14.65–22.18 0.40
Cho GM 3.27 .30 2.74–3.68 3.16 .37 2.64–3.88 0.52
WM 2.84 .13 2.66–3.02 2.90 .46 2.26–3.58 0.99
Splenium 2.72 .73 1.96–3.98 2.33 .47 1.63–3.03 0.21
Cr GM 17.09 .78 16.20–18.85 17.40 1.51 16.14–21.30 0.68
WM 9.73 .59 9.03–10.78 9.05 .93 7.91–10.54 0.02
Splenium 10.90 2.65 7.79–15.71 7.78 2.03 5.03–11.88 0.02
Glx GM 25.52 1.51 23.69–27.43 27.73 2.32 22.96–31.40 0.02
WM 11.00 1.67 9.16–13.69 9.34 2.23 5.47–13.29 0.08
Glu GM 20.36 .73 18.91–21.37 21.44 1.51 19.04–23.61 0.06
WM 9.33 1.11 8.09–11.27 8.39 1.04 6.40–10.22 0.08
Univariate tests were only conducted after finding significant MANOVA effects, however all values are reported (mmol=kg water).
FIG. 2. Graphical representation of mean metabolite levels for patients with mTBI (black bars) compared to healthy controls
(gray bars). Error bars indicate standard deviations. Data are presented for N-acetylaspartate (NAA), choline (Cho), creatine-
phosphocreatine (Cr), glutamine (Glx), and glutamate (Glu) in gray matter (GM), white matter (WM), and splenium (Spl)
(
*
Denotes statistically significant result).
1640 GASPAROVIC ET AL.
mTBI, which focused on metabolite ratios rather than absolute
concentration levels. Govindaraju and colleagues (2004) as-
sessed ratio scores (NAA:Cr and NAA:Cho) from 25 single
predominantly gray- or white-matter voxels, and reported
that both ratios were reduced in mTBI patients for two of the
white matter voxels. Similarly, Vagnozzi and associates (2008)
recently reported reduced NAA:Cr for mTBI patients in an-
terior periventricular white matter. In contrast to these find-
ings, our results indicated higher WM Cr for mTBI patients,
with no statistically significant differences in WM NAA.
These results for absolute metabolite concentrations suggest
that previous reports of a lower NAA:Cr ratio in mTBI may be
attributable to higher Cr rather than lower NAA. Vagnozzi
and co-workers (2008) also observed that NAA:Cr ratios are
higher in patients with additional time after injury. We ob-
served a similar trend in our sample, with larger NAA:Cr
ratios occurring with greater time after injury. Taken together,
these observations highlight the dynamic nature of metabolite
changes after mTBI.
Our observation that WM Cr is higher in the semi-acute
phase of recovery from mTBI stands in contrast to findings
that Cr is unchanged in the chronic phase of more severe
injury (Friedman et al., 1998). The most likely interpretation of
this differing pattern of results is that our patients were
assessed at a transient state of metabolic alteration, before
eventual normalization. The Cr buffer system plays a critical
role in maintaining cellular ATP stores in cells with high and
fluctuating energy demands, such as neurons. Anti-apoptotic,
antioxidant, and osmoregulatory roles have also been pro-
posed for Cr, and Cr dietary supplementation has been shown
to improve brain function in healthy subjects and in several
brain disorders (Andres et al., 2008), including TBI (Sakellaris
et al., 2006; Sakellaris et al., 2008; Sullivan et al., 2000). The
group differences in Cr levels observed in the present study
may be related to an elevated demand for energy production
after trauma, since Cr and phosphocreatine are essential for
maintaining adequate ATP stores in the brain, and have been
shown to be depressed in animal models of mTBI (Vagnozzi
et al., 2005). However, a greater demand for Cr in its non-
energy-related roles following brain injury may account for
the observed elevation.
While elevated Glx or Glu has been reported following
more severe TBI injury, potentially indicating excitotoxicity
(Alves et al., 2005; Ashwal et al., 2004; Babikian et al., 2006;
Koura et al., 1998; Shutter et al., 2004; Vespa et al., 1998;
Zauner et al., 1996), the present study revealed that Glx was
significantly lower in GM and slightly higher (at a trend level)
in WM after mTBI. A similar type of effect was also observed
for Glu in supplementary analyses. Low Glx or Glu levels
have been reported in some chronic pathologies, including
multiple sclerosis (Chard et al., 2002), schizophrenia (Tayoshi
et al., 2008), and major depression (Capizzano et al., 2007), but
to our knowledge, not in otherwise healthy-appearing brain
tissue following mTBI. In one recent study of rodent models of
mild and severe brain injury induced by graded microinjec-
tion of amino-3-hydroxy-5-methyl-4-isoxazole propionic acid,
a decrease in the activity of glutaminase, the enzyme that
converts Gln to Glu in the presynaptic neuron, and an increase
in the activity of glutamine synthetase, the enzyme that con-
verts Gln to Glu in astrocytes, was observed (Ramonet et al.,
2004). The authors hypothesized that this alteration may be a
protective adaptation against glutamate excitotoxicity. How-
ever, neither Glu nor Gln levels were measured in that study,
and it is not clear that the present results can be interpreted
similarly. Nonetheless, given the complexity and delicate
regulation of neural Glu-Gln metabolism in normally-
functioning tissue (Ramonet et al., 2004), downregulating or
diverting Glu production to alternative pathways after mild
injury, such as toward glutathione or energy production,
might represent an adaptive response to avoid excitotoxicity
following mTBI. The finding of lower GM Glx and Glu might
also be consistent with a temporary diversion of Glu from
dendritic regions for protective or repair purposes.
One important limitation of the current study was the
sample size. Though similar to the sample sizes in previously
published
1
H-MRS studies on mTBI, this sample was small,
and thus our findings need to be replicated in a larger inde-
pendent sample. The effects of our small sample size were
most notable in our neuropsychological results, where effect
sizes for both emotional and executive functions were in the
large range (Cohen et al., 1992), and would have likely
reached conventional levels of statistical significance with a
larger sample size. Likewise our observation that NAA:Cr
ratios correlated with time post-injury might also reach con-
ventional levels of statistical significance with the addition of
a few more patients to the sample. Nonetheless, in spite of the
sample size, significant differences were observed between
mTBI patients and matched controls for several
1
H-MRS
metabolites, indicating the sensitivity of
1
H-MRS to metabo-
lite alterations that might serve as biomarkers of mTBI. To
maximize the sensitivity of our methods, an effort was made
to correct the metabolite and water reference signals for par-
tial volume effects, with greater attention given to varying
tissue water density and relaxation times than has been given
previously (Gasparovic et al., 2006). It is worth noting that the
molal concentrations (mol=kg of tissue water) reported in this
study will be inherently higher than the molar values
(moles=volume of tissue) reported by others, and will depend
on assumptions concerning the tissue water density and re-
laxation times (Gasparovic et al., 2006). However, the use of
tissue water as a concentration standard may provide a truer
estimate of the effective concentration of the metabolite,
and obviates many of the sources of error inherent to the
use of external references. This approach allowed us to ob-
serve group differences between Glx and Glu levels that were
in the range of 5–12%, while NAA levels differed by 1% or
less.
In summary, the present study provides preliminary evi-
dence for the sensitivity of
1
H-MRS to detect subtle pertur-
bations in neurometabolism following mTBI, and that the
direction of these effects may differ in gray and white matter.
The observation of lower gray-matter Glx and Glu, with a
trend for higher white-matter Glx and Glu, may be consistent
with a temporary diversion of Glu from dendritic regions for
protective or repair purposes following mild injury. However,
this hypothesis is highly speculative and will require further
testing in animal models of injury. Our findings of higher Cr
in mTBI subjects may reflect greater energy demands after
injury, and furthermore suggest that interpretations of
NAA:Cr must be made with the appropriate caveats in this
population. Future studies should utilize prospective imaging
protocols to determine if these metabolite levels normalize as
a function of the spontaneous recovery process that is typical
in mTBI.
NEUROMETABOLISM IN MILD TBI 1641
Acknowledgments
This research was supported by grants from The Mind
Research Network (DOE grant no. DE-FG02-99ER62764), and
from the National Institutes of Health (grant R24HD050836
and R21-NS064464-01A1). Special thanks to Diana South and
Cathy Smith for assistance with data collection.
Author Disclosure Statement
No conflicting financial interests exist.
References
Alves, O.L., Bullock, R., Clausen, T., Reinert, M., and Reeves,
T.M. (2005). Concurrent monitoring of cerebral electrophysi-
ology and metabolism after traumatic brain injury: An ex-
perimental and clinical study. J. Neurotrauma 22, 733–749.
Andres, R.H., Ducray, A.D., Schlattner, U., Wallimann, T., and
Widmer, H.R. (2008). Functions and Effects of creatine in the
central nervous system. Brain Res. Bull. 76, 329–343.
Arciniegas, D.B., Anderson, C.A., Topkoff, J., and McAllister,
T.W. (2005). Mild traumatic brain injury: A neuropsychiatric
approach to diagnosis, evaluation, and treatment. Neu-
ropsychiatr. Dis. Treat. 1, 311–327.
Ashburner, J., and Friston, K.J. (2005). Unified segmentation.
NeuroImage 26, 839–851.
Ashwal, S., Holshouser, B., Tong, K., Serna, T., Osterdock, R.,
Gross, M., and Kido, D. (2004). Proton MR spectroscopy de-
tected glutamate=glutamine is increased in children with
traumatic brain injury. J. Neurotrauma. 21, 1539–1552.
Babikian, T., Freier, M.C., Ashwal, S., Riggs, M.L., Burley, T.,
and Holshouser, B.A. (2006). MR spectroscopy: Predicting
long-term neuropsychological outcome following pediatric
TBI. J. Magn. Reson. Imaging 24, 801–811.
Baslow, M.H., Hrabe, J., and Guilfoyle, D.N. (2007). Dynamic
relationship between neurostimulation and N-acetylaspartate
metabolism in the human visual cortex: Evidence that NAA
functions as a molecular water pump during visual stimula-
tion. J. Mol. Neurosci. 32, 235–245.
Belanger, H.G., Curtiss, G., Demery, J.A., Lebowitz, B.K., and
Vanderploeg, R.D. (2005). Factors moderating neuropsycho-
logical outcomes following mild traumatic brain injury: A
meta-analysis. J. Int. Neuropsychol. Soc. 11, 215–227.
Belanger, H.G., Vanderploeg, R.D., Curtiss, G., and Warden,
D.L. (2007). Recent neuroimaging techniques in mild trau-
matic brain injury. J. Neuropsychiatry Clin. Neurosci. 19, 5–20.
Bigler, E.D. (2008). Neuropsychology and clinical neuroscience
of persistent post-concussive syndrome. J. Int. Neuropsychol.
Soc. 14, 1–22.
Brooks, W.M., Friedman, S.D., and Gasparovic, C. (2001).
Magnetic resonance spectroscopy in traumatic brain injury.
J. Head Trauma Rehabil. 16, 149–164.
Capizzano, A.A., Jorge, R.E., Acion, L.C., and Robinson, R.G.
(2007). In vivo proton magnetic resonance spectroscopy in
patients with mood disorders: A technically oriented review.
J. Magn. Reson. Imaging 26, 1378–1389.
Chard, D.T., Griffin, C.M., McLean, M.A., Kapeller, P., Kapoor,
R., Thompson, A.J., and Miller, D.H. (2002). Brain metabolite
changes in cortical grey and normal-appearing white matter in
clinically early relapsing-remitting multiple sclerosis. Brain
125, 2342–2352.
Cimatti, M. (2006). Assessment of metabolic cerebral damage
using proton magnetic resonance spectroscopy in mild trau-
matic brain injury. J. Neurosurg. Sci. 50, 83–88.
Cohen, B.A., Inglese, M., Rusinek, H., Babb, J.S., Grossman, R.I.,
and Gonen, O. (2007). Proton MR spectroscopy and MRI-
volumetry in mild traumatic brain injury. A.J.N.R. Am. J.
Neuroradiol. 28, 907–913.
Cohen, J. (1992). A power primer. Psychol. Bull. 112, 155–159.
DeFord, S.M., Wilson, M.S., Rice, A.C., Clausen, T., Rice, L.K.,
Barabnova, A., Bullock, R., and Hamm, R.J. (2002). Repeated
mild brain injuries result in cognitive impairment in B6c3f1
Mice. J. Neurotrauma 19, 427–438.
Di, X., Gordon, J., and Bullock, R. (1999). Fluid percussion brain
injury exacerbates glutamate-induced focal damage in the rat.
J. Neurotrauma. 16, 195–201.
Friedman, S.D., Brooks, W.M., Jung, R.E., Hart, B.L., and Yeo,
R.A. (1998). Proton MR spectroscopic findings correspond to
neuropsychological function in traumatic brain injury.
A.J.N.R. Am. J. Neuroradiol. 19, 1879–1885.
Garnett, M.R., Blamire, A.M., Corkill, R.G., Cadoux-Hudson,
T.A., Rajagopalan, B., and Styles, P. (2000a). Early proton
magnetic resonance spectroscopy in normal-appearing brain
correlates with outcome in patients following traumatic
brain injury. Brain 123(Pt. 10), 2046–2054.
Garnett, M.R., Blamire, A.M., Rajagopalan, B., Styles, P., and
Cadoux-Hudson, T.A. (2000b). Evidence for cellular damage
in normal-appearing white matter correlates with injury se-
verity in patients following traumatic brain injury: A magnetic
resonance spectroscopy study. Brain. 123(Pt. 7), 1403–1409.
Gasparovic, C., Song, T., Devier, D., Bockholt, H.J., Caprihan, A.,
Mullins, P.G., Posse, S., Jung, R.E., and Morrison, L.A. (2006).
Use of tissue water as a concentration reference for proton
spectroscopic imaging. Magn. Reson. Med. 55, 1219–1226.
Globus, M.Y., Alonso, O., Dietrich, W.D., Busto, R., and Gins-
berg, M.D. (1995). Glutamate release and free radical pro-
duction following brain injury: Effects of posttraumatic
hypothermia. J. Neurochem. 65, 1704–1711.
Govindaraju, V., Gauger, G.E., Manley, G.T., Ebel, A., Meeker,
M., and Maudsley, A.A. (2004). Volumetric proton spectro-
scopic imaging of mild traumatic brain injury. A.J.N.R. Am. J.
Neuroradiol. 25, 730–737.
Hartley, C.E., Varma, M., Fischer, J.P., Riccardi, R., Strauss, J.A.,
Shah, S., Zhang, S., and Yang, Z.J. (2008). Neuroprotective
effects of erythropoietin on acute metabolic and pathological
changes in experimentally induced neurotrauma. J. Neuro-
surg. 109, 708–714.
Hattingen, E., Raab, P., Franz, K., Lanfermann, H., Setzer, M.,
Gerlach, R., Zanella, F.E., and Pilatus, U. (2008). Prognostic
value of choline and creatine in who grade II gliomas. Neu-
roradiology 50, 759–767.
Hoge, C.W., McGurk, D., Thomas, J.L., Cox, A.L., Engel, C.C.,
and Castro, C.A. (2008). Mild traumatic brain injury in U.S.
soldiers returning from Iraq. N. Engl. J. Med. 358, 453–463.
Inglese,
M.,
Li, B.S., Rusinek, H., Babb, J.S., Grossman, R.I., and
Gonen, O. (2003). Diffusely elevated cerebral choline and
creatine in relapsing-remitting multiple sclerosis. Magn. Reson.
Med. 50, 190–195.
Jennett, B. (1998). Epidemiology of head injury. Arch. Dis. Child.
78, 403–406.
Kirov, I., Fleysher, L., Babb, J.S., Silver, J.M., Grossman, R.I., and
Gonen, O. (2007). Characterizing ‘mild’ in traumatic brain
injury with proton MR spectroscopy in the thalamus: Initial
findings. Brain Inj. 21, 1147–1154.
Koura, S.S., Doppenberg, E.M., Marmarou, A., Choi, S., Young,
H.F., and Bullock, R. (1998). Relationship between excitatory
amino acid release and outcome after severe human head in-
jury. Acta Neurochir. Suppl. 71, 244–246.
1642 GASPAROVIC ET AL.
Kreis, R., Ernst, T., and Ross, B.D. (1993). Absolute quantitation
of water and metabolites in the human brain. II. Metabolite
concentrations. J. Magn. Reson. B. 102, 9–19.
Matser, J.T., Kessels, A.G., Jordan, B.D., Lezak, M.D., and Troost,
J. (1998). Chronic traumatic brain injury in professional soccer
players. Neurology 51, 791–796.
McKenna, M.C. (2007). The glutamate-glutamine cycle is not
stoichiometric: Fates of glutamate in brain. J. Neurosci. Res.
85, 3347–3358.
Mlynarik, V., Gruber, S., and Moser, E. (2001). Proton T (1) and
T (2) relaxation times of human brain metabolites at 3 Tesla.
N.M.R. Biomed. 14, 325–331.
Moffett, J.R., Ross, B., Arun, P., Madhavarao, C.N., and Nam-
boodiri, A.M. (2007). N-acetylaspartate in the CNS: From
neurodiagnostics to neurobiology. Prog. Neurobiol. 81, 89–
131.
Mullins, P.G., Chen, H., Xu, J., Caprihan, A., and Gasparovic, C.
(2008). Comparative reliability of proton spectroscopy tech-
niques designed to improve detection of J-coupled metabo-
lites. Magn. Reson. Med. 60, 964–969.
Munoz Maniega, S., Cvoro, V., Armitage, P.A., Marshall, I.,
Bastin, M.E., and Wardlaw, J.M. (2008). Choline and creatine
are not reliable denominators for calculating metabolite ratios
in acute ischemic stroke. Stroke 39, 2467–2469.
Nilsson, P., Hillered, L., Ponten, U., and Ungerstedt, U. (1990).
Changes in cortical extracellular levels of energy-related me-
tabolites and amino acids following concussive brain injury in
rats. J. Cereb. Blood Flow Metab. 10, 631–637.
Oldfield, R.C. (1971). The assessment and analysis of handed-
ness: The Edinburgh Inventory. Neuropsychologia 9, 97–113.
Palmer, A.M., Marion, D.W., Botscheller, M.L., Swedlow, P.E.,
Styren, S.D., and DeKosky, S. T. (1993). Traumatic brain
injury-induced excitotoxicity assessed in a controlled cortical
impact model. J. Neurochem. 61, 2015–2024.
Pouwels, P.J., and Frahm, J. (1997). Differential distribution of
NAA and NAAG in human brain as determined by quanti-
tative localized proton MRS. N.M.R. Biomed. 10, 73–78.
Provencher, S.W. (1993). Estimation of metabolite concentrations
from localized in vivo proton NMR spectra. Magn. Reson.
Med. 30, 672–679.
Ramonet, D., Rodriguez, M.J., Fredriksson, K., Bernal, F., and
Mahy, N. (2004). In vivo neuroprotective adaptation of the
glutamate=glutamine cycle to neuronal death. Hippocampus
14, 586–594.
Rooney, W.D., Johnson, G., Li, X., Cohen, E.R., Kim, S.G.,
Ugurbil, K., and Springer, C.S., Jr. (2007). Magnetic field and
tissue dependencies of human brain longitudinal 1H2O re-
laxation in vivo. Magn. Reson. Med. 57, 308–318.
Sakellaris, G., Kotsiou, M., Tamiolaki, M., Kalostos, G., Tsapaki,
E., Spanaki, M., Spilioti, M., Charissis, G., and Evangeliou, A.
(2006). Prevention of complications related to traumatic brain
injury in children and adolescents with creatine administra-
tion: An open label randomized pilot study. J. Trauma 61,
322–329.
Sakellaris, G., Nasis, G., Kotsiou, M., Tamiolaki, M., Charissis,
G., and Evangeliou, A. (2008). Prevention of traumatic head-
ache, dizziness and fatigue with creatine administration. A
pilot study. Acta Paediatr. 97, 31–34.
Shutter, L., Tong, K.A., and Holshouser, B.A. (2004). Proton MRS in
acute traumatic brain injury: Role for glutamate=glutamine and
choline for outcome prediction. J. Neurotrauma. 21, 1693–1705.
Shutter, L., Tong, K.A., Lee, A., and Holshouser, B.A. (2006). prog-
nostic role of proton magnetic resonance spectroscopy in acute
traumatic brain injury. J. Head Trauma Rehabil. 21, 334–349.
Son, B.C., Park, C.K., Choi, B.G., Kim, E.N., Choe, B.Y., Lee, K.S.,
Kim M.C., and Kang, J.K. (2000). Metabolic changes in peri-
contusional oedematous areas in mild head injury evaluated
by 1h MRS. Acta Neurochir. Suppl. 76, 13–16.
Sullivan, P.G., Geiger, J.D., Mattson, M.P., and Scheff, S.W.
(2000). Dietary supplement creatine protects against traumatic
brain injury. Ann Neurol. 48, 723–729.
Tavazzi, B., Vagnozzi, R., Signoretti, S., Amorini, A.M., Belli, A.,
Cimatti, M., Delfini, R., Di Pietro, V., Finocchiaro, A., and
Lazzarino, G. (2007). Temporal window of metabolic brain
vulnerability to concussions: Oxidative and nitrosative stres-
ses—part II. Neurosurgery 61, 390–395; discussion 395–396.
Tayoshi, S., Sumitani, S., Taniguchi, K., Shibuya-Tayoshi, S.,
Numata, S., Iga, J.I., Nakataki, M., Ueno, S.I., Harada, M., and
Ohmori, T. (2008). Metabolite changes and gender differences
in schizophrenia using 3-Tesla proton magnetic resonance
spectroscopy ((1)H-MRS). Schizophr Res. 108, 69–77.
Thierry, A.G.M., Huisman, T.A., Schwamm, L.H., Schaefer,
P.W., Koroshetz, W.J., Shetty-Alva, N., Ozsunar, Y., Wu, O.,
and Sorensen, A.G. (2004). Diffusion tensor imaging as po-
tential biomarker of white matter injury in diffuse axonal in-
jury. A.J.N.R. Am. J. Neuroradiol. 25, 370–376.
Vagnozzi, R., Signoretti, S., Tavazzi, B., Cimatti, M., Amorini,
A.M., Donzelli, S., Delfini, R. and Lazzarino, G. (2005). Hy-
pothesis of the postconcussive vulnerable brain: Experimental
evidence of its metabolic occurrence. Neurosurgery 57, 164–
171; discussion 164–171.
Vagnozzi, R., Signoretti, S., Tavazzi, B., Floris, R., Ludovici, A.,
Marziali, S., Tarascio, G., Amorini, A.M., Di Pietro, V., Delfini,
R. and Lazzarino, G. (2008). Temporal window of metabolic
brain vulnerability to concussion: A pilot 1h-magnetic reso-
nance spectroscopic study in concussed athletes—Part III.
Neurosurgery 62, 1286–1295; discussion 1295–1286.
Vagnozzi, R., Tavazzi, B., Signoretti, S., Amorini, A.M., Belli, A.,
Cimatti, M., Delfini, R., Di Pietro, V., Finocchiaro, A., and
Lazzarino, G. (2007). Temporal window of metabolic brain
vulnerability to concussions: Mitochondrial-related impair-
ment—Part
I.
Neurosurgery 61, 379–388; discussion 388–379.
Vespa, P., Prins, M., Ronne-Engstrom, E., Caron, M., Shalmon,
E., Hovda, D.A., Martin, N.A., and Becker, D.P. (1998). In-
crease in extracellular glutamate caused by reduced cerebral
perfusion pressure and seizures after human traumatic brain
injury: A microdialysis study. J. Neurosurg. 89, 971–982.
Vymazal, J., Righini, A., Brooks, R.A., Canesi, M., Mariani, C.,
Leonardi, M., and Pezzoli, G. (1999). T1 and T2 in the brain of
healthy subjects, patients with Parkinson disease, and patients
with multiple system atrophy: Relation to iron content.
Radiology 211, 489–495.
Yeo, R.A., Phillips, J.P., Jung, R.E., Brown, A.J., Campbell, R.C.,
and Brooks, W.M. (2006). Magnetic resonance spectroscopy
detects brain injury and predicts cognitive functioning in
children with brain injuries. J. Neurotrauma 23, 1427–1435.
Zauner, A., Bullock, R., Kuta, A.J., Woodward, J., and Young,
H.F. (1996). Glutamate release and cerebral blood flow after
severe human head injury. Acta Neurochir Suppl. 67, 40–44.
Address correspondence to:
Charles Gasparovic, Ph.D.
The Mind Research Network
Pete and Nancy Domenici Hall
1101 Yale Boulevard, N.E.
Albuquerque, NM 87131
E-mail: chuck@unm.edu
NEUROMETABOLISM IN MILD TBI 1643
    • "Reduced NAA/Cr has been associated with gray matter atrophy (Cohen et al., 2007 ) and correlated with posttraumatic headache (Sarmento et al., 2009). Increased Cr levels in white matter has been correlated with abnormal executive functioning and emotional distress (Gasparovic et al., 2009). Serial MRS evaluation of 11 athletes with concussion compared to matched controls demonstrated concomitant decrease in NAA and "
    [Show abstract] [Hide abstract] ABSTRACT: Mild traumatic brain injury (TBI) is common but accurate diagnosis and defining criteria for mild TBI and its clinical consequences have been problematic. Mild TBI causes transient neurophysiologic brain dysfunction, sometimes with structural axonal and neuronal damage. Biomarkers, such as newer imaging technologies and protein markers, are promising indicators of brain injury but are not ready for clinical use. Diagnosis relies on clinical criteria regarding depth and duration of impaired consciousness and amnesia. These criteria are particularly difficult to confirm at the least severe end of the mild TBI continuum, especially when relying on subjective, retrospective accounts. The postconcussive syndrome is a controversial concept because of varying criteria, inconsistent symptom clusters and the evidence that similar symptom profiles occur with other disorders, and even in a proportion of healthy individuals. The clinical consequences of mild TBI can be conceptualized as two multidimensional disorders: (1) a constellation of acute symptoms that might be termed early phase post-traumatic disorder (e.g., headache, dizziness, imbalance, fatigue, sleep disruption, impaired cognition), that typically resolve in days to weeks and are largely related to brain trauma and concomitant injuries; (2) a later set of symptoms, a late phase post-traumatic disorder, evolving out of the early phase in a minority of patients, with a more prolonged (months to years), sometimes worsening set of somatic, emotional, and cognitive symptoms. The later phase disorder is highly influenced by a variety of psychosocial factors and has little specificity for brain injury, although a history of multiple concussions seems to increase the risk of more severe and longer duration symptoms. Effective early phase management may prevent or limit the later phase disorder and should include education about symptoms and expectations for recovery, as well as recommendations for activity modifications. Later phase treatment should be informed by thoughtful differential diagnosis and the multiplicity of premorbid and comorbid conditions that may influence symptoms. Treatment should incorporate a hierarchical, sequential approach to symptom management, prioritizing problems with significant functional impact and effective, available interventions (e.g., headache, depression, anxiety, insomnia, vertigo). © 2015 Elsevier B.V. All rights reserved.
    Full-text · Article · Dec 2015
    • "Published results show " considerable variations in the approaches " [8] and therefore a large span for absolute concentrations . For the metabolite Naa with the best reproducibility , the concentration inside white matter in healthy subjects can differ from 9.7 to 18.4 mmol/l3031323334. The values depend on sequence design, acquisition system, post-processing algorithm as well as post-processing parameters , relaxation corrections, internal or external references or subject gender, subject age and the position inside the brain [24]. "
    [Show abstract] [Hide abstract] ABSTRACT: Post processing for brain spectra has a great influence on the fit quality of individual spectra, as well as on the reproducibility of results from comparable spectra. This investigation used pairs of spectra, identical in system parameters, position and time assumed to differ only in noise. The metabolite amplitudes of fitted time domain spectroscopic data were tested on reproducibility for the main brain metabolites. Proton spectra of white matter brain tissue were acquired with a short spin echo time of 30 ms and a moderate repetition time of 1500 ms at 1.5 T. The pairs were investigated with one time domain post-processing algorithm using different parameters. The number of metabolites, the use of prior knowledge, base line parameters and common or individual damping were varied to evaluate the best reproducibility. The protocols with most reproducible amplitudes for N-acetylaspartate, creatine, choline, myo-inositol and the combined Glx line of glutamate and glutamine in lesion free white matter have the following common features: common damping of the main metabolites, a baseline using only the points of the first 10 ms, no additional lipid/macromolecule lines and Glx is taken as the sum of separately fitted glutamate and glutamine. This parameter set is different to the one delivering the best individual fit results. All spectra were acquired in “lesion free” (no lesion signs found in MR imaging) white matter. Spectra of brain lesions, for example tumors, can be drastically different. Thus the results are limited to lesion free brain tissue. Nevertheless the application to studies is broad, because small alterations in brain biochemistry of lesion free areas had been detected nearby tumors, in patients with multiple sclerosis, drug abuse or psychiatric disorders. Main metabolite amplitudes inside healthy brain can be quantified with a normalized root mean square deviation around 5 % using CH 3 of creatine as reference. Only the reproducibility of myo-inositol is roughly twice as bad. The reproducibility should be similar using other references like internal or external water for an absolute concentration evaluation and are not influenced by relaxation corrections with literature values.
    Full-text · Article · Dec 2015
    • "A few studies have shown a metabolic decline in patients with mTBI [21, 22] . For example, Nacetylaspartic acid (NAA)232425, creatine [26] , and glutamate-glutamine levels [27, 28] have been shown to be reduced in such patients. NAA is a marker of neuronal mitochondria metabolism, as a close association exists between NAA, oxygen , and adenosine triphosphate levels [29, 30]. "
    Full-text · Dataset · Sep 2015 · BMC Medical Imaging
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