Neuroimaging and Therapeutics in Movement Disorders
Thomas Eckert*†and David Eidelberg†‡
*Department of Neurology II and Psychiatry, University of Magdeburg, Germany;†Center for Neurosciences, Institute for
Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York 11030; and‡Department of Neurology,
New York University School of Medicine, New York, New York 10016
Summary: In this review, we discuss the role of neuroimaging
in assessing treatment options for movement disorders, partic-
ularly Parkinson’s disease (PD). Imaging methods to assess
dopaminergic function have recently been applied in trials of
potential neuroprotective agents. Other imaging methods using
regional metabolism and/or cerebral perfusion have been re-
cently introduced to quantify the modulation of network activ-
ity as an objective marker of the treatment response. Both
imaging strategies have provided novel insights into the mech-
anisms underlying a variety of pharmacological and stereotaxic
surgical treatment strategies for PD and other movement dis-
orders. Key Words: Movement disorders, Parkinson’s disease,
Huntington’s disease, Tourette’s syndrome, dystonia, positron
emission tomography (PET), magnetic resonance imaging
Movement disorders are a group of syndromes char-
acterized by an impairment of the regulation of voluntary
motor activity without deficits of force, cerebellar func-
tion, or sensation. This class of neurological diseases
includes hypokinetic disorders associated with a slowing
of movements such as Parkinson’s disease (PD), as well
as hyperkinetic disorders characterized by involuntary
abnormal movements such as Huntington’s disease
(HD), torsion dystonia, and tic disorders. Generally, the
clinical manifestations of movement disorders result
from dysfunction of the basal ganglia. Although his-
topathologic studies reveal specific neurodegenerative
changes in some of these diseases (e.g., PD and HD), the
pathologic basis for many movement disorders remains
unknown. Various neuroimaging techniques have been
used to visualize pathological changes in these disorders.
Radiotracer imaging techniques using positron emission
tomography (PET) and single-photon emission com-
puted tomography (SPECT) imaging can be used to eval-
uate and quantify changes in specific neurochemical sys-
tems.1,2Alternatively, disease-related changes in local
brain function can be assessed with generalized tracers
for regional cerebral metabolism and blood flow.3,4
Brain imaging in movement disorders was originally
introduced to visualize the pathological changes associ-
ated with different clinical syndromes.5Subsequently,
these techniques have been used in longitudinal studies
designed to assess disease progression6,7and the effects
of potential neuroprotective strategies.8–10Lately, func-
tional imaging has also been applied in the objective
assessment of symptomatic treatment responses.11–14In
addition to providing an objective descriptor of the treat-
ment response, brain imaging can also link clinical out-
come to alterations in regional brain function.15
In this review, we describe functional neuroimaging
strategies to evaluate and monitor therapeutic interven-
tions for movement disorders. Because PD is the most
common and broadly studied of these disorders, this
review will focus on the use of imaging to assess treat-
ment options for this condition.
DOPAMINERGIC IMAGING IN PARKINSON?S
DISEASE: NEUROPROTECTION TRIALS
PET and SPECT assessments of nigrostriatal dopaminer-
dopaminergic imaging can be conducted using the follow-
ing approaches: 1. [18F]-fluorodopa (FDOPA) PET to eval-
Address correspondence and reprint requests to Dr. David Eidelberg,
Center for Neurosciences, Institute for Medical Research, North Shore-
Long Island Jewish Health System, 350 Community Drive, Manhasset,
NY 11030. E-mail: firstname.lastname@example.org.
NeuroRx?: The Journal of the American Society for Experimental NeuroTherapeutics
Vol. 2, 361–371, April 2005 © The American Society for Experimental NeuroTherapeutics, Inc.
uate the uptake and conversion from fluorodopa to fluoro-
dopamine by the aromatic acid decarboxylase (AADC); 2.
[11C]-dihydrotetrabenazine (DTBZ) PET to assess the den-
sity of monoamine-containing synaptic vesicles; and 3. a
variety of radiolabeled cocaine derivatives [e.g., [123I]-2?-
carbomethyl-3?-(4-iodophenyl) tropane (?-CIT)] to quan-
tify the expression of the dopamine transporter (DAT),
which facilitates the release and reabsorption of dopamine
in the nigrostriatal intersynaptic cleft. The relative merits of
these agents have been summarized elsewhere.1,2
Radiotracer imaging of the presynaptic nigrostriatal
dopaminergic system has been used to assess the rate of
disease progression deterioration in PD. FDOPA PET
and ?-CIT SPECT show a 4% to 13% yearly reduction in
baseline putamen uptake compared with 0–2.5% in
healthy controls in longitudinal studies.6,7This technique
has also been used to estimate the duration of the pre-
clinical period of PD using a linear regression analysis.16
Striatal FDOPA measurements correlate with dopamine
cell counts measured in postmortem specimens17,18and
striatal DAT binding decreases with age in healthy vol-
unteers and PD patients.19–21
The precise determination of neuroprotective treat-
ment effects represents a major challenge in current
movement disorders therapeutics. Although well estab-
lished in clinical trials for PD, clinical rating scales like the
Unified Parkinson’s Disease Rating Scale (UPDRS)22may
not be sufficiently sensitive to detect subtle changes in rates
of progression, as are likely to be encountered in neuropro-
tective trials. Moreover, clinical ratings reflect long-term
symptomatic effects, which are apt to persist following
protracted medication washout.9
Imaging assessment of presynaptic nigrostriatal func-
tion has been used for evaluation of disease progression
in PD patients (see above) and may therefore provide a
useful adjunct to clinical assessment in assessing disease
progression in pharmacologic therapeutic trials of poten-
tial neuroprotective agents.2Two large randomized,
blinded studies have employed imaging to investigate
disease progression in patients receiving dopamine ago-
nists relative to those treated with levodopa. In both
studies, imaging based descriptors of neuroprotection di-
verged from the clinical outcome measures. The
CALM-PD study investigated early treatment with levo-
dopa versus pramipexole.10It was found that PD patients
receiving levodopa did better clinically, although they
experienced more motor complications. The REAL-PET
study disclosed similar results in a comparison of levo-
dopa with ropinirole. By contrast, presynaptic dopami-
nergic imaging with ?-CIT SPECT (CALM-PD) and
FDOPA PET (REAL-PET) revealed slower declines
with agonist treatment. Similarly, in the subsequent
ELLDOPA trial,9subjects treated with high-dose levo-
dopa had the best clinical outcome even following up to
4 weeks of medication washout, despite a more rapid
decline in putamen DAT binding as measured by ?-CIT
These trials reveal discrepancies between the clinical
assessment and radiotracer-based imaging of the presyn-
aptic dopaminergic system, and raise the question of
comparability of these measures as neuroprotection out-
come variables. Even though most imaging descriptors
of nigrostriatal dopaminergic function correlate with in-
dependent disease severity measures (see Ravina et al.2
for review), these techniques do not directly assess the
number or density of nigral dopaminergic neurons.
Moreover, these radiotracer-based imaging methods re-
quire simplifying assumptions for the acquisition and
analysis of data. Thus, the results of these imaging stud-
ies may be affected by factors other than the primary
biological process under study.
A critical issue in this regard is the occurrence of
possible temporal up- and downregulation of neuropep-
tides and receptors that can affect the results of imaging
studies. A study comparing FDOPA, DTBZ, and a DAT
ligand, in the same PD subjects revealed a relative up-
regulation of AADC and downregulation of DAT.23This
suggests that surviving neurons may synthesize more
dopamine but perhaps also take up less from the synaptic
cleft. Dopaminergic treatment can change the amount of
available dopamine at the synaptic level, which may
differentially influence the regulation of AADC and
DAT. Although a number of studies have not revealed an
effect of dopaminergic treatment on presynaptic dopa-
minergic imaging measures,8,24,25other evidence sug-
gests that dopaminergic treatment can affect these pa-
rameters in PD patients.26,27Specifically, Guttman et
al.27reported a decline in DAT binding after treatment
withlevodopa, which was
pramipexole treatment. Similar treatment effects in the
CALM-PD study could have contributed to the differ-
ences in imaging measures that were observed with the
pramipexole and levodopa treatment groups. Further in-
vestigations with larger numbers of patients will be re-
quired to characterize time-dependent changes in the reg-
ulation of neuropeptides or receptors that might occur
with treatment. Indeed, the optimal duration of medica-
tion washout before imaging will need to be determined
before further studies of this sort can be pursued effec-
Lastly, the evaluation of disease progression in PD
with radiotracer imaging has relied upon the notion that
pathology is limited to the nigrostriatal neurons. This
assumption may be overly restrictive given that other
neural pathways are likely to be involved with disease
progression, even at early clinical phases of the illness.
Dopaminergic imaging with PET has also been used to
assess the efficacy of embryonic cell transplantation for
PD. The presence of increases in putamen FDOPA up-
take after transplantation is consistent with graft surviv-
ECKERT AND EIDELBERG362
NeuroRx?, Vol. 2, No. 2, 2005
al.28,29These localized changes may underlie the devel-
opment of post-transplantation dyskinesia in some
patients.30Additionally, graft function has been directly
demonstrated using PET imaging and
(RAC) displacement methods.31Nonetheless, the mean-
ing of these post-transplantation changes on PET is un-
clear given that motor performance does not necessarily
improve in these patients (e.g.,32). Whereas the ultimate
clinical role of embryonic dopamine cell transplantation
for PD is unclear, imaging methodologies are likely to
provide useful biomarkers in future trials of cell-based
METABOLIC BRAIN NETWORKS IN
PD patients exhibit characteristic changes in regional
glucose utilization on PET studies conducted with [18F]-
fluorodeoxyglucose (FDG) in the resting state.3,34For-
mal multiregion, multisubject network approaches using
principal components analysis (PCA)3,35have been em-
ployed to identify patterns of regional metabolism that
are associated with PD3,36and other movement disorders
(e.g., Huntington’s Disease,37
Tourette syndrome).40PCA extracts multiple, statisti-
cally independent components that account, singly or in
combination, for the majority of the variability in the
regional PET data. The technique also quantifies the
expression of these patterns in individual subjects.41,42
Specific criteria have been developed to determine
whether one or more patterns are “disease-related,” i.e.,
have significantly different expression in patients relative
to controls.36,38For instance, employing this mathemat-
ical-statistical approach, we identified a specific regional
metabolic network in PD patients scanned in the resting
state with FDG PET.3This PD-related covariance pattern
(PDRP) was characterized by increased glucose metab-
olism of the lentiform nucleus, thalamus, and brainstem
as well as decreased glucose metabolism of the lateral
premotor cortex and the supplementary motor area
(SMA). This characteristic pattern (FIG. 1A) 1) has been
validated in multiple populations of unmedicated PD
patients4,36,43; 2) can be detected early in the disease
course34; and 3) correlates consistently with disease se-
verity and duration.3,44We also found that PDRP activity
correlates with nigrostriatal dopamine deficiency as de-
termined by [18F]-fluorodopa PET34,45and with internal
pallidal (GPi) single unit activity recorded during sur-
gery.46Blinded prospective PDRP calculations show that
this pattern not only discriminates PD patients from nor-
mal subjects (FIG. 1B),36but can also distinguish be-
tween patients with PD and atypical parkinsonian syn-
dromes.3,4,34Additionally, preliminary results from a
longitudinal PET study of early stage PD conducted at
our center suggest that PDRP expression may also be
sensitive to disease progression (FIG. 2). These results
suggest that network imaging with FDG PET may have
certain advantages in clinical trial settings: 1) FDG PET
is becoming commonplace in North American, Euro-
pean, and Japanese medical centers. Although the tech-
FIG. 1. A: Parkinson’s disease-related metabolic pattern. This
pattern of regional metabolic covariation was identified by net-
work analysis of [18F]-fluorodeoxyglucose (FDG) PET scans from
20 PD patients and 20 age-matched normal volunteer sub-
jects.14,123This PDRP (representing the first principal compo-
nent, which accounted for 20.7% of the subject ? voxel varia-
tion) was characterized by pallidal, thalamic, pontine, and
cerebellar hypermetabolism associated with metabolic decre-
ments in the lateral premotor and posterior parietal areas. The
display represents voxels that contribute significantly to the net-
work at p ? 0.001, and that were demonstrated to be reliable by
bootstrap estimation (p ? 0.0001). [Voxels with positive region
weights (metabolic increases) are color coded from red to yel-
low; those with negative region weights (metabolic decreases)
are color coded from blue to purple. The numbers under each
slice are in millimeters, relative to the anterior-posterior commis-
sure line.] B: Prospectively computed PDRP scores accurately
discriminate subjects by blinded network analysis. Left: PDRP
expression (subject scores) in the 20 PD patients (filled circles)
and 20 control subjects (open circles) described above. Network
activity was significantly increased in the PD cohort (p ?
0.00001). Right: In a prospective individual case analysis (see
text), we computed the expression of the PDRP (see panel A) in
14 subsequent PD patients (filled circles) and 14 control subjects
(open circles). These computations were conducted using an
automated routine that was blind to diagnostic category.38,41As
in the original analysis, prospectively computed PDRP scores
were significantly elevated in the disease group (p ? 0.00001).
NEUROIMAGING, THERAPEUTICS IN MOVEMENT DISORDERS363
NeuroRx?, Vol. 2, No. 2, 2005
nology has focused upon oncology uses, brain imaging
of neurodegenerative conditions can be performed con-
veniently employing the same imaging platforms. In-
deed, pattern analysis can be performed remotely on
images transferred electronically from remote sites.13,14
2) Prospective quantification of network activity can be
performed on a case by case basis,41even using scans of
cerebral perfusion obtained using less expensive imaging
devices such as SPECT.4This methodology may ulti-
mately be applicable to perfusion-based magnetic reso-
nance imaging (MRI)47, thereby totally eliminating the
need for tracer injections and radiation exposure. 3) Net-
work approaches may be useful in quantifying the effects
of therapy by scanning subjects before and after treat-
ment (see below). Given the capacity of metabolic im-
aging to assess multiple networks in single subjects, this
technique may be able to parse out motor and nonmotor
treatment effects on brain function.43,48
Network modulation by therapy
Network quantification during therapeutic interven-
tions for PD may provide an objective means of assess-
ing treatment effects on brain function. This imaging
approach may be particularly useful in screening new
agents for symptomatic therapy in that network modula-
tion during treatment can be detected with sample sizes
as small as seven subjects. Initial assessments of treat-
ment responses employing this network approach were
conducted in patients undergoing stereotaxic surgical in-
terventions. Significant declines in PDRP activity were
first observed following unilateral pallidotomy.11Subse-
quent studies investigating the effects of deep brain stim-
ulation (DBS) of the internal segment of the globus
pallidus (GPi),12ablation of the subthalamic nucleus
(STN),13,14and STN DBS,15revealed significant sup-
pression of PDRP network activity as a common feature
of these interventions (FIG. 3). Indeed, unilateral surgery
resulted in significant reductions in PDRP expression in
the ipsilateral hemisphere, and network modulation was
not present contralateral to intervention. In most cases,
the degree of network reduction correlated significantly
with improvement in standardized motor ratings,12,13
suggesting the potential role of this approach in the ob-
jective assessment of treatment effects in a blinded trials
setting. Even though all these interventions suppressed
the PDRP, there were differences in magnitude of PDRP
reduction, depending upon the surgical target (GPi, STN,
or Vim thalamic) or mode of treatment (ablation or
DBS). In agreement with the general clinical impression
that STN is superior to GPi as a treatment target,49,50the
magnitude of PDRP suppression was higher for interven-
tions involving the former structure. The finding that
PDRP expression is not altered by Vim DBS for severe
PD tremor is also consistent with prior studies suggesting
different mechanisms underlying the akinetic-rigid and
tremulous manifestation of the disease51,52in PD patients
undergoing Vim DBS for intractable PD tremor.53Sig-
nificant reductions in PDRP expression have also been
observed following pharmacological intervention. Com-
parison of FDG PET scans before and during levodopa
infusion revealed a significant reduction of PDRP ex-
pression during treatment.54As in the surgical interven-
FIG. 2. Longitudinal changes in early stage PD: dopaminergic
loss and network evolution. Top: Mean expression of the PD-
related metabolic covariance pattern (PDRP, see FIG. 1A) at
baseline, and at the second (24 months) and third (36 months)
time points as part of our longitudinal FDG PET study of early
stage Parkinson’s disease (solid line). PDRP values increased
significantly over time (p ? 0.005; repeated measures ANOVA).
Bottom: Mean putamen DAT binding measured by [18F]-fluoro-
propyl ?CIT PET in the same PD patients scanned longitudinally
at the three time points (dashed line). DAT binding was ex-
pressed as percentage of the normal mean value for 15 age-
matched control subjects. Unlike the longitudinal increases that
occurred with PDRP expression, DAT binding declined over time
(p ? 0.04, reading a minimum of 30% normal for the putamen.
tions. Bar graph illustrating relative changes in the expression of
the PD-related metabolic covariance pattern (? PDRP; see text),
during antiparkinsonian therapy with levodopa infusion,54and
unilateral ventral pallidotomy,11pallidal and STN DBS,12,15and
subthalamotomy13(filled bars). For the surgical interventions, ?
PDRP reflects changes in network activity in the operated hemi-
spheres. With levodopa infusion, the PDRP changes were aver-
aged across hemispheres. [The control data (open bars) repre-
sent: 1) ? PDRP values in the unoperated contralateral
hemispheres (CN) of the surgical patients scanned in the un-
medicated state; and 2) PDRP changes with unilateral Vim tha-
lamic stimulation for tremor-predominant PD53]. [Asterisks rep-
resent p values with respect to the untreated condition (paired
Student’s t test). *: p ? 0.01; **: p ? 0.005].
Network modulation with antiparkinsonian interven-
ECKERT AND EIDELBERG 364
NeuroRx?, Vol. 2, No. 2, 2005
tions, reductions in PDRP scores correlated with clinical
improvement measured in standardized motor rating
scales. Interestingly, the degree of PDRP suppression
observed during levodopa infusion was similar in mag-
nitude to that encountered with STN DBS15(FIG. 3).
This observation is consistent with human and animal
studies suggesting mechanistic similarities between the
two forms of treatment.55In aggregate, these results
indicate that the expression of disease-related brain net-
works may constitute a useful biomarker for objectively
assessing outcomes in trials of new treatment approaches
for PD and other movement disorders.
FDG PET has been used in other investigations to
assess regional changes in glucose utilization occurring
with levodopa treatment56as well as with STN DBS.57
Rather than network analysis, these studies used statis-
tical parametric mapping (SPM) to localize mean treat-
ment effects subsequent to the two interventions. Al-
though useful in identifying brain regions affected by
therapy, this approach does not use regional covariation
to quantify the network-wide changes that might occur
during treatment. Comprehensive imaging investigations
using PET in conjunction with different analytical tools
will be of value in assessing the comparative efficacy of
new and established treatments for PD.
Network analysis of imaging data can also be used to
identify specific metabolic topographies associated with
different manifestations of disease such as tremor and
akinesia.51Additionally, we used PCA to characterize
patterns associated with visuospatial and memory func-
tion in PD,43,48as well as affective symptoms. We
found48that mnemonic functioning correlated with a
specific metabolic pattern involving parietal decrements,
covarying with increases in temporal cortex, pons, and
cerebellum. By contrast, dysphoria in PD patients was
associated with metabolic decrements in dorsolateral
prefrontal, orbitofrontal, and anterior cingulate cortex. A
subsequent FDG PET study43used network analysis to
demonstrate that brain metabolism in PD could be seg-
regated into two discrete (orthogonal) networks, relating
respectively to motor and nonmotor disease manifesta-
tions. The first pattern correlated with clinical ratings for
bradykinesia and was topographically similar to the pre-
viously characterized PDRP. The second pattern was
characterized by relative ventromedial frontal, hip-
pocampal, and striatal hypometabolism, as well as me-
diodorsal thalamic hypermetabolism. Scores for this pat-
tern correlated significantly with indices of executive
function.43These studies emphasize the versatility of the
network approach in clinically applied brain imaging. By
quantifying the activity of multiple networks in single
resting state images, this method can allow investigators
to assess the differential effects of treatment on parallel
neural systems using simple, widely available, and po-
tentially automated scanning routines.
The differential diagnosis of parkinsonian syndromes
on clinical grounds, even if considered to be “gold stan-
dard,” may be limited, especially at early disease stag-
es.58–62It may be argued that the inadvertent inclusion
of atypical “look alikes” may not impact the results of
large-scale randomized blind studies. However, given
that up to 30% of patients enrolled may not have classi-
cal PD63even with rigid inclusion criteria in early dis-
ease stages,61one can only assume that the assessment of
treatment effects would be improved by eliminating het-
erogenous cohorts of variant patients from all treatment
arms. Correct diagnosis early in disease is also important
for the patients themselves because prognosis64–66and
treatment options67–72can differ substantially between
patients with PD and patients with atypical parkinsonian
Various imaging techniques for differential diagnosis
have been employed.5These include imaging of glucose
metabolism using FDG PET; imaging of presynaptic
dopaminergic function with presynaptic ligands;34,73–76
imaging of the postsynaptic dopaminergic D2-receptor
SPECT and raclopride PET;77–79imaging of cardiac
sympathetic denervation;80,81as well as various MRI
Examples of the use of these methods have appeared
in the literature of the past decade. Presynaptic dopami-
nergic imaging has been shown to differentiate between
patients with parkinsonian syndromes on one side, and
healthy control subjects and essential tremor patients on
the other.34,73–76Postsynaptic dopamine D2-receptor im-
aging may help in distinguishing between PD and atyp-
ical parkinsonian syndromes.77–79Imaging of the cardiac
sympathetic system reveals denervation in PD patients,
even in early disease stages, and may differentiate be-
tween patients with multiple system atrophy (MSA) and
PD.80,81However, these techniques are not generally
able to discriminate between the various atypical parkin-
Several MRI techniques have been employed for dif-
ferential diagnosis in parkinsonian syndromes. Even
though routine MRI shows characteristic changes as an
atrophic putamen and a hyperintensive rim of the puta-
men in MSA patients, these changes are observed in only
half of subjects suspected as having this diagnosis.82
Recent MRI studies have revealed decreases of the ap-
parent diffusion coefficient of the putamen in MSA pa-
tients83as well as decreases in the magnetization transfer
ratio in regions of neuronal degeneration in MSA and
progressive supernuclear palsy (PSP) patients.84These
noninvasive methods may ultimately prove helpful in
differentiating these syndromes from PD.
NEUROIMAGING, THERAPEUTICS IN MOVEMENT DISORDERS365
NeuroRx?, Vol. 2, No. 2, 2005
Evaluation of regional glucose utilization with FDG
PET has revealed characteristic metabolic patterns for
the different parkinsonian syndromes. As described
above, clinical PD is characterized by a distinctive met-
abolic pattern involving increased activity in the puta-
men/globus pallidus, thalamus, cerebellum, and brain-
stem, as well as relative reductions in cortical motor
regions.3,34,85,86Characteristic patterns have been asso-
ciated with other parkinsonian syndromes. MSA is char-
acterized by metabolic decreases in the lentiform nucleus
and cerebellum.87–92By contrast, PSP is associated with
decreased metabolism in midline frontal cortical areas
and the brainstem.74,93–95Corticobasal ganglionic degen-
eration (CBGD) is associated with relatively reduced
glucose metabolism in many cortical areas, including the
insula and in the basal ganglia of the hemisphere con-
tralateral to the most affected side.96–98
Results of an ongoing study99have shown that these
characteristic patterns can prospectively distinguish be-
tween patients with PD, MSA, PSP, CBGD, and healthy
age-matched control subjects. In a large cohort of pa-
tients who underwent FDG PET imaging for purposes of
differential diagnosis of parkinsonian disorders at North
Shore University Hospital between 1995 and 2001, a
probable/likely clinical diagnosis was achieved at fol-
low-up by blind review undertaken independently by two
movement disorder specialists. Blind reading of the
scans by either visual inspection or a single subject sta-
tistical parametric mapping (SPM) approach were com-
pared in terms of their ability to conform with an ulti-
mate diagnosis achieved at follow-up. Overall, correct
imaging diagnosis was obtained in about 90% of all
subjects using the SPM approach and in about 84% by
blind visual inspection of the FDG scans.99These results
emphasize the excellent potential of FDG PET imaging to
augment clinical assessments in the evaluation of subjects
for potential enrollment in pharmaceutical trials for PD.
METABOLIC NETWORKS IN OTHER
MOVEMENT DISEASES: POTENTIAL
SURROGATE MARKERS FOR
In contrast to PD where PET and SPECT techniques
can provide quantitative descriptors of dopaminergic
dysfunction, other movement disorders are not associ-
ated with specific, localized neurochemical abnormalities
that can be measured in vivo. However, the quantification
of disease-related patterns of glucose metabolism may
provide unique data regarding diagnosis and treatment
effects in situations in which other imaging approaches
are lacking or insufficient. Indeed, we have used FDG
PET to describe unique metabolic networks associated
with Huntington’s disease,37,100idiopathic torsion dys-
tonia,38,39and Tourette syndrome.40,101
Huntington’s disease (HD) is a hereditary neurodegen-
erative disorder characterized by progressively worsen-
ing abnormalities of movement and cognition. While
investigating the glucose metabolism in HD patients, we
identified an HD-related pattern (HDRP). HDRP is char-
acterized by caudate and putaminal hypometabolism but
also includes mediotemporal metabolic reductions as
well as relative metabolic increases in the occipital cor-
tex (FIG. 4A). Assessment of presymptomatic HD gene
carriers revealed that HDRP expression is not only ele-
vated before the development of clinical symptoms, but
may also be increased significantly at a time when stri-
atal D2receptor binding is still normal.37Results of an
ongoing longitudinal study show that HDRP expression
is also a valuable measure for disease progression in HD
patients (FIG. 4B).100It is currently not known whether
HDRP expression is more sensitive to advancing neuro-
degeneration in the preclinical period than either striatal
D2binding102or MRI-based measures of local atro-
phy.103Quite possibly, the most accurate assessment of
disease progression in the preclinical period of HD will
be achieved with multiple imaging approaches in com-
Torsion dystonia is a movement disorder characterized
by sustained muscle contractions with twisting and re-
petitive movements or abnormal postures. Although pri-
mary torsion dystonia (PTD) is linked to several genetic
mutations,105there is no consistent histopathology asso-
ciated with this disorder. Nonetheless, our FDG PET
studies revealed a reproducible pattern associated with
primary dystonia.38,39,106This torsion dystonia-related
pattern (TDRP) is characterized by hypermetabolism of
the basal ganglia, cerebellum, and SMA (FIG. 5A).38
Whereas this characteristic pattern was initially identi-
fied in affected PTD patients,106its presence was subse-
quently confirmed in genotyped subjects, even without
clinical manifestations.38,39,107Further studies revealed
subtle behavioral impairments in nonmanifesting DYT1
carriers,108as well as abnormalities in brain activation
responses during movement and nonmotor learning. It is
therefore possible that TDRP expression can represent an
endophenotype for dystonia, which may be useful in
gene identification in at risk populations (FIG. 5B). The
clinical spectrum of dystonia may indeed be broader than
previously suspected,109with separate structural/func-
tional abnormalities underlying the motor and nonmotor
manifestations of the disorder.110,111How genotypic and
phenotypic patterns are altered by the treatment of dystonia,
particularly DBS, is a topic of ongoing investigation.
Gilles de la Tourette’s syndrome (TS) is a movement
disorder characterized by multiple motor and vocal tics
ECKERT AND EIDELBERG366
NeuroRx?, Vol. 2, No. 2, 2005
of varying intensity. Despite its well-known clinical pre-
sentation, the histopathology and the underlying physio-
logic mechanisms mediating the clinical manifestations
of TS are unknown. An early network analysis of FDG
PET data in TS40revealed the disease-related metabolic
patterns. One pattern was characterized by metabolic
increases in the lateral premotor and supplementary mo-
tor association cortices and the midbrain, perhaps sec-
ondary abnormal spontaneous movements occurring dur-
ing PET imaging. The second pattern was characterized
by metabolic decreases in the caudate and thalamus, with
less pronounced decrements in the lentiform nucleus and
the hippocampus. This network correlated significantly
with Tourette Syndrome Global Scale (TSGS) ratings.
Importantly, this TS-related topography has recently
(HDRP). This pattern of regional metabolic covariation was iden-
tified by network analysis of [18F]-fluorodeoxyglucose (FDG) PET
scans from 10 presymptomatic HD gene carriers and 20 age-
matched normal volunteer subjects. This HDRP (representing
the first principal component, which accounted for 18% of the
subject ? voxel variation) discriminated carriers from controls
(p ? 0.0001) and was characterized by relative metabolic de-
creases in the striatum, associated with increases in the tempo-
ral cortex, insula, and occipital association cortex. [The display
represents voxels that contribute significantly to the network at
p ? 0.01. Voxels with positive region weights (metabolic in-
creases) are color coded from red to yellow; those with negative
region weights (metabolic decreases) are color coded from blue
to purple. The numbers under each slice are in millimeters, rel-
ative to the anterior-posterior commissure line.] B: Preclinical
progression of HDRP: dopaminergic loss and network evolution.
Bottom: Mean expression of the HD-related metabolic covari-
ance pattern (HDRP, see panel A) in preclinical gene carriers
scanned at baseline and at 18 months as part of our longitudinal
FDG PET study (solid line). HDRP values increased significantly
over this time period (p ? 0.03; paired t test). Top: Mean RAC
binding in the same preclinical HD cohort scanned at baseline
and at 18 months (dashed line). Striatal D2 receptor binding was
abnormally reduced at both time points (p ? 0.001 relative to
gene negative controls). RAC binding declined significantly be-
tween the two time points (p ? 0.05; paired t test).
A: Huntington’s disease-related metabolic pattern
FIG. 5. A: Torsion dystonia-related metabolic pattern (TDRP).
This pattern of regional metabolic covariation was identified by
network analysis of [18F]-fluorodeoxyglucose (FDG) PET scans
from nonmanifesting DYT1 gene carriers and control subjects
(see text). This TDRP was characterized by bilateral covarying
metabolic increases in the putamen, extending into the globus
pallidus (GP), the SMA, and the cerebellar hemisphere. Subject
scores for this pattern discriminated the DYT1 carriers from
controls (p ? 0.002). The display represents voxels that contrib-
ute significantly to the network at p ? 0.001. Voxels with positive
region weights (metabolic increases) are color coded red. B:
TDRP activity in dystonia genotypes (prospective FDG/PET
study). Scatter diagram of torsion dystonia-related pattern sub-
ject scores computed prospectively in six new nonaffected DYT1
gene carriers, six DYT6 gene carriers, seven dopa-responsive
dystonia (DRD) patients, and 13 control subjects. Subject scores
were abnormally elevated in DYT1 (p ? 0.001) and DYT6 carriers
(p ? 0.007), but not in DRD patients (p ? 0.4). The error bars
indicate subgroups standard errors of the mean. Circles, normal
controls; squares, subjects with genotypes associated with pri-
mary torsion dystonia; triangles, DRD patients; open symbols,
clinically nonmanifesting subjects; filled symbols, affected dys-
NEUROIMAGING, THERAPEUTICS IN MOVEMENT DISORDERS 367
NeuroRx?, Vol. 2, No. 2, 2005
been identified in a subsequent group of patients scanned
on a modern tomograph. A voxel-based network analysis
of these FDG PET data101confirmed that this pattern
predicted TSGS ratings, indicating its potential as a sur-
rogate marker for this condition in future clinical trials.
This may be particularly relevant in movement disorders
such as TS in which fully objective treatment markers
are not available.
OTHER IMAGING APPROACHES POSSIBLY
APPLICABLE TO MONITOR TREATMENT
Investigations have been conducted to assess changes
in brain activation responses during therapy. These im-
aging studies have been conducted with
riety of interventions such as dopaminergic therapy (apo-
morphine,112levodopa113,114) and DBS.115These studies
have been reviewed recently.116,117Despite the elegance
of this approach, the demands of experimental control of
task performance during dynamic imaging make this
strategy less desirable for the study of treatment effects
in large populations. The assessment of brain activation
responses with treatment requires the performance of
specified motor and/or cognitive tasks in different treat-
ment and, thus, changes of brain perfusion or BOLD
signal during task performance are dependent on factors
such as movement frequency and force118,119as well as
cognitive load.120These parameters are particularly dif-
ficult to control in a motorically impaired patient popu-
lation. Moreover, task equivalence within subjects across
treatment conditions is necessary if the activation com-
parisons are truly to reflect the inherent effects of therapy
on brain function. Nonetheless, carefully controlled ac-
tivation studies may shed light on the mechanisms by
which different therapeutic strategies modulate neural
circuitry during motor performance and learning.121,122
Indeed, network analysis of these data has revealed re-
producible patterns of regional activation that may be
used to assess the effects of therapy on higher order
15O) PET and functional MRI methods during a va-
New imaging methodologies are emerging124that may
be useful in the quantitative assessment of potential neu-
roprotective agents. Recent MRI studies in parkinsonian
syndromes have revealed changes in the apparent diffu-
sion coefficient83and the magnetization transfer ratio,84
perhaps reflecting aspects of the underlying pathological
features of these conditions. Such techniques may provide
interesting biomarkers for neurodegenerative processes that
may be suitable for neuroprotective trials. Diffusion tensor
imaging124has revealed microstructural changes in the
white matter of DYT1 gene carrier in the subgyral white
matter of the sensorimotor cortex.110This suggests that
abnormal anatomical connectivity in cortical-subcortical
pathways may be important in determining whether carriers
are more or less likely to develop clinical signs of disease,
and may be the basis for variable penetrance in some in-
herited neurological conditions.107Similar mechanisms
may be involved in other movement disorders in which
conventional methods have failed to reveal consistent func-
tional or anatomical abnormalities.
Perfusion-weighted MR imaging124represents another
emerging imaging method with potential promise in the
assessment of disease progression and treatment effects.
Because cerebral blood flow and metabolism are gener-
ally coupled in neurodegenerative disorders, changes in
cerebral perfusion may be used as a simple noninvasive,
radiation-free alternative to PET and SPECT for network
quantification in the resting state (e.g.,4). Although ap-
pealing, more research is needed to determine the ulti-
mate utility of these methods in the context of clinical
Functional neuroimaging has contributed greatly to
our understanding of the pathophysiology and natural
history of the movement disorders. Recently, these meth-
odologies have generated a variety of imaging-based
biomarkers for the assessment of both symptomatic and
neuroprotective therapies for this class of neurological
disease. Although the quantification of dopaminergic
function with imaging has shown great promise in clin-
ical trials for PD, this approach may be less appropriate
for other movement disorders in which other neurotrans-
mitter systems may be involved. In this review, we dem-
onstrate the use of alternative metabolic imaging strate-
gies for the objective measurement of treatment effects
in PD. In addition to providing novel imaging biomark-
ers for motor and nonmotor manifestations of PD, this
general approach may be of value in the determination of
treatment outcomes in other movement disorders in
which a specific neurochemical pathology is not pre-
1. Brooks DJ. Positron emission tomography and single-photon
emission computed tomography in central nervous system drug
development. NeuroRx 2:226–236, 2005.
2. Ravina B, Eidelberg D, Ahlskog JE, Albin R, Brooks DJ, Carbon
M, et al. The role of radiotracer imaging in Parkinson’s disease.
Neurology 64:208–215, 2005.
3. Eidelberg D, Moeller JR, Dhawan V, Spetsieris P, Takikawa S,
Ishikawa T, et al. The metabolic topography of parkinsonism.
J Cereb Blood Flow Metab 14:783–801, 1994.
4. Feigin A, Antonini A, Fukuda M, De Notaris R, Benti R, Pezzoli
G, et al. Tc-99m ethylene cysteinate dimer SPECT in the differ-
ential diagnosis of parkinsonism. Mov Disord 17:1265–1270,
ECKERT AND EIDELBERG368
NeuroRx?, Vol. 2, No. 2, 2005
5. Eckert T, Eidelberg D. The role of functional neuroimaging in the
differential diagnosis of idiopathic Parkinson’s disease and mul-
tiple system atrophy. Clin Auton Res 14:84–91, 2004.
6. Brooks DJ. Imaging end points for monitoring neuroprotection in
Parkinson’s disease. Ann Neurol 53:S110–S118, 2003.
7. Marek K, Jennings D, Seibyl J. Dopamine agonists and Parkin-
son’s disease progression: what can we learn from neuroimaging
studies. Ann Neurol 53:S160–166, 2003.
8. Parkinson-Study-Group. Dopamine transporter brain imaging to
assess the effects of pramipexole vs levodopa on Parkinson dis-
ease progression. JAMA 287:1653–1661, 2002.
9. Fahn S, Oakes D, Shoulson I, Kieburtz K, Rudolph A, Lang A,
Olanow CW, Tanner C, Marek K; Parkinson Study Group. Levo-
dopa and the progression of Parkinson’s disease. N Engl J Med
10. Whone AL, Watts RL, Stoessl AJ, Davis M, Reske S, Nahmias C,
et al. Slower progression of Parkinson’s disease with ropinirole
versus levodopa: the REAL-PET study. Ann Neurol 54:93–101,
11. Eidelberg D, Moeller JR, Ishikawa T, Dhawan V, Spetsieris P,
Silbersweig D, et al. Regional metabolic correlates of surgical
outcome following unilateral pallidotomy for Parkinson’s dis-
ease. Ann Neurol 39:450–459, 1996.
12. Fukuda M, Mentis MJ, Ma Y, Dhawan V, Antonini A, Lang AE,
et al. Networks mediating the clinical effects of pallidal brain
stimulation for Parkinson’s disease: a PET study of resting-state
glucose metabolism. Brain 124:1601–1609, 2001.
13. Su PC, Ma Y, Fukuda M, Mentis MJ, Tseng HM, Yen RF, et al.
Metabolic changes following subthalamotomy for advanced Par-
kinson’s disease. Ann Neurol 50:514–520, 2001.
14. Tros ˇt M, Su PC, Barnes A, Su SL, Yen RF, Tseng HM, et al.
Evolving metabolic changes during the first postoperative year
after subthalamotomy. J Neurosurg 99:872–878, 2003.
15. Devous MD Sr. Single-photon emission computed tomography in
neurotherapeutics. NeuroRx 2:237–249, 2005.
16. Morrish PK, Rakshi JS, Bailey DL, Sawle GV, Brooks DJ. Mea-
suring the rate of progression and estimating the preclinical pe-
riod of Parkinson’s disease with [18F]dopa PET. J Neurol Neu-
rosurg Psychiatry 64:314–319, 1998.
17. Pate BD, Kawamata T, Yamada T, McGeer EG, Hewitt KA,
Snow BJ, et al. Correlation of striatal fluorodopa uptake in the
MPTP monkey with dopaminergic indices. Ann Neurol 34:331–
18. Snow B, Tooyama I, McGeer E, Yamada T, Calne D, Takahashi
H, et al. Human positron emission tomographic [18F]fluorodopa
studies correlate with dopamine cell counts and levels. Ann Neu-
rol 34:324–330, 1993.
19. Tissingh G, Bergmans P, Booij J, Winogrodzka A, Stoof JC,
Wolters EC, et al. [123I]?-CIT single-photon emission tomogra-
phy in Parkinson’s disease reveals a smaller decline in dopamine
transporters with age than in controls. Eur J Nucl Med 24:1171–
20. Kazumata K, Dhawan V, Chaly T, Antonini A, Margouleff C,
Belakhlef A, et al. Dopamine transporter imaging with fluorine-
18-FPCIT and PET. J Nucl Med 39:1521–1530, 1998.
21. Dhawan V, Eidelberg D. SPECT imaging in Parkinson’s disease.
Adv Neurol 86:205–213, 2001.
22. Fahn S, Elton R, UPDRS Development Committee. Unified Par-
kinson’s Disease Rating Scale. In: Fahn S, Marsden CD, Calne D,
eds. Recent developments in Parkinson?s disease. New York:
MacMillan, pp 153–163, 1987.
23. Lee CS, Samii A, Sossi V, Ruth TJ, Schulzer M, Holden JE, et al.
In vivo positron emission tomographic evidence for compensa-
tory changes in presynaptic dopaminergic nerve terminals in Par-
kinson’s disease. Ann Neurol 47:493–503, 2000.
24. Innis RB, Marek KL, Sheff K, Zoghbi S, Castronuovo J, Feigin
A, et al. Effect of treatment with L-dopa/carbidopa or L-selegiline
on striatal dopamine transporter SPECT imaging with [123I]beta-
CIT. Mov Disord 14:436–442, 1999.
25. Nurmi E, Bergman J, Eskola O, Solin O, Hinkka SM, Sonninen
P, et al. Reproducibility and effect of levodopa on dopamine
transporter function measurements: a [18F]CFT PET study.
J Cereb Blood Flow Metab 20:1604–1609, 2000.
26. Ahlskog JE, Uitti RJ, O’Connor MK, Maraganore DM, Matsu-
moto JY, Stark KF, et al. The effect of dopamine agonist therapy
on dopamine transporter imaging in Parkinson’s disease. Mov
Disord 14:940–946, 1999.
27. Guttman M, Stewart D, Hussey D, Wilson A, Houle S, Kish S.
Influence of L-dopa and pramipexole on striatal dopamine trans-
porter in early PD. Neurology 56:1559–1564, 2001.
28. Nakamura T, Dhawan V, Chaly T, Fukuda M, Ma Y, Breeze R,
et al. Blinded positron emission tomography study of dopamine
cell implantation for Parkinson’s disease. Ann Neurol 50:181–
29. Olanow CW, Goetz CG, Kordower JH, Stoessl AJ, Sossi V, Brin
MF, et al. A double-blind controlled trial of bilateral fetal nigral
transplantation in Parkinson’s disease. Ann Neurol 54:403–414,
30. Ma Y, Feigin A, Dhawan V, Fukuda M, Shi Q, Greene P, et al.
Dyskinesia after fetal cell transplantation for parkinsonism: a PET
study. Ann Neurol 52:628–634, 2002.
31. Piccini P, Brooks DJ, Bjorklund A, Gunn RN, Grasby PM,
Rimoldi O, et al. Dopamine release from nigral transplants visu-
alized in vivo in a Parkinson’s patient. Nat Neurosci 2:1137–
32. Freed CR, Greene PE, Breeze RE, Tsai WY, DuMouchel W, Kao
R, et al. Transplantation of embryonic dopamine neurons for
severe Parkinson’s disease. N Engl J Med 344:710–719, 2001.
33. Lindvall O. Stem cells for cell therapy in Parkinson’s disease.
Pharmacol Res 47:279–287, 2003.
34. Eidelberg D, Moeller JR, Ishikawa T, Dhawan V, Spetsieris P,
Chaly T, et al. Early differential diagnosis of Parkinson’s disease
with 18F-fluorodeoxyglucose and positron emission tomography.
Neurology 45:1995–2004, 1995.
35. Alexander GE, Moeller JR. Application of the scaled subprofile
model to functional imaging in neuropsychiatric disorders: a prin-
cipal component approach to modeling brain function in disease.
Hum Brain Mapp 2:1–16, 1994.
36. Moeller JR, Nakamura T, Mentis MJ, Dhawan V, Spetsieres P,
Antonini A, et al. Reproducibility of regional metabolic covari-
ance patterns: comparison of four populations. J Nucl Med 40:
37. Feigin A, Leenders KL, Moeller JR, Missimer J, Kuenig G,
Spetsieris P, et al. Metabolic network abnormalities in early Hun-
tington’s disease: an [(18)F]FDG PET study. J Nucl Med 42:
38. Eidelberg D, Moeller JR, Antonini A, Kazumata K, Nakamura T,
Dhawan V, et al. Functional brain networks in DYT1 dystonia.
Ann Neurol 44:303–312, 1998.
39. Tros ˇt M, Carbon M, Edwards C, Ma Y, Raymond D, Mentis MJ,
et al. Primary dystonia: is abnormal functional brain architecture
linked to genotype? Ann Neurol 52:853–856, 2002.
40. Eidelberg D, Moeller JR, Antonini A, Kazumata K, Dhawan V,
Budman C, et al. The metabolic anatomy of Tourette’s syndrome.
Neurology 48:927–934, 1997.
41. Eidelberg D, Moeller JR, Ishikawa T, Dhawan V, Spetsieris P,
Chaly T, et al. Assessment of disease severity in parkinsonism
with fluorine-18-fluorodeoxyglucose and PET. J Nucl Med 36:
42. Moeller JR, Ishikawa T, Dhawan V, Spetsieris P, Mandel F,
Alexander GE, et al. The metabolic topography of normal aging.
J Cereb Blood Flow Metab 16:385–398, 1996.
43. Lozza C, Baron JC, Eidelberg D, Mentis MJ, Carbon M, Marie
RM. Executive processes in Parkinson’s disease: FDG-PET and
network analysis. Hum Brain Mapp 22:236–245, 2004.
44. Moeller JR, Eidelberg D. Divergent expression of regional met-
abolic topographies in Parkinson’s disease and normal ageing.
Brain 120:2197–2206, 1997.
45. Eidelberg D, Moeller JR, Dhawan V, Sidtis JJ, Ginos JZ, Strother
SC, et al. The metabolic anatomy of Parkinson’s disease: com-
positron emission tomographic studies. Mov Disord 5:203–213,
46. Eidelberg D, Moeller JR, Kazumata K, Antonini A, Sterio D,
Dhawan V, et al. Metabolic correlates of pallidal neuronal activity
in Parkinson’s disease. Brain 120:1315–1324, 1997.
NEUROIMAGING, THERAPEUTICS IN MOVEMENT DISORDERS 369
NeuroRx?, Vol. 2, No. 2, 2005
47. Alsop DC, Casement M, Press D. Increased hippocampal perfu-
sion in early Alzheimer?s disease. Proc Intl Soc Mag Reson Med
48. Mentis MJ, McIntosh AR, Perrine K, Dhawan V, Berlin B, Feigin
A, et al. Relationships among the metabolic patterns that correlate
with mnemonic, visuospatial, and mood symptoms in Parkinson’s
disease. Am J Psychiatry 159:746–754, 2002.
49. Hamani C, Saint-Cyr JA, Fraser J, Kaplitt M, Lozano AM. The
subthalamic nucleus in the context of movement disorders. Brain
50. Volkmann J Deep brain stimulation for the treatment of Parkin-
son’s disease. J Clin Neurophysiol 21:6–17, 2004.
51. Antonini A, Moeller JR, Nakamura T, Spetsieris P, Dhawan V,
Eidelberg D. The metabolic anatomy of tremor in Parkinson’s
disease. Neurology 51:803–810, 1998.
52. Fukuda M, Barnes A, Simon ES, Holmes A, Dhawan V, Giladi N,
et al. Thalamic stimulation for parkinsonian tremor: correlation
between regional cerebral blood flow and physiological tremor
characteristics. Neuroimage 21:608–615, 2004.
53. Tros ˇt M, Simon ES, Dhawan V, Okulski J, Fodstad H, Eidelberg
D. Clinical and metabolic brain changes in tremor predominant
Parkinson’s disease patients treated with Vim deep brain stimu-
lation. Mov Disord 199:S383, 2004.
54. Feigin A, Fukuda M, Dhawan V, Przedborski S, Jackson-Lewis
V, Mentis MJ, et al. Metabolic correlates of levodopa response in
Parkinson’s disease. Neurology 57:2083–2088, 2001.
55. Brown RG, Dowsey PL, Brown P, Jahanshahi M, Pollak P,
Benabid AL, et al. Impact of deep brain stimulation on upper limb
akinesia in Parkinson’s disease. Ann Neurol 45:473–488, 1999.
56. Berding G, Odin P, Brooks DJ, Nikkhah G, Matthies C, Peschel
T, et al. Resting regional cerebral glucose metabolism in ad-
vanced Parkinson’s disease studied in the off and on conditions
with [(18)F]FDG-PET. Mov Disord 16:1014–1022, 2001.
57. Hilker R, Voges J, Weisenbach S, Kalbe E, Burghaus L, Ghaemi
M, et al. Subthalamic nucleus stimulation restores glucose me-
tabolism in associative and limbic cortices and in cerebellum:
evidence from a FDG-PET study in advanced Parkinson’s dis-
ease. J Cereb Blood Flow Metab 24:7–16, 2004.
58. Litvan I, Agid Y, Goetz C, Jankovic J, Wenning GK, Brandel JP,
et al. Accuracy of the clinical diagnosis of corticobasal degener-
ation: a clinicopathologic study. Neurology 48:119–125, 1997.
59. Litvan I, Goetz CG, Jankovic J, Wenning GK, Booth V, Bartko
JJ, et al. What is the accuracy of the clinical diagnosis of multiple
system atrophy? A clinicopathologic study. Arch Neurol 54:937–
60. Litvan I, Booth V, Wenning GK, Bartko JJ, Goetz CG, McKee A,
et al. Retrospective application of a set of clinical diagnostic
criteria for the diagnosis of multiple system atrophy. J Neural
Transm 105:217–227, 1998.
61. Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of
diagnosis of parkinsonian syndromes in a specialist movement
disorder service. Brain 125:861–870, 2002.
62. Osaki Y, Wenning GK, Daniel SE, Hughes A, Lees AJ, Mathias
CJ, et al. Do published criteria improve clinical diagnostic accu-
racy in multiple system atrophy? Neurology 59:1486–1491,
63. Hughes AJ, Daniel SE, Blankson S, Lees A. A clinicopathologic
study of 100 cases of Parkinson’s disease. Arch Neurol 50:140–
64. Diamond SG, Markham CH, Hoehn MM, McDowell FH,
Muenter MD. Multi-center study of Parkinson mortality with
early versus later dopa treatment. Ann Neurol 22:8–12, 1987.
65. Golbe LI, Davis PH, Schoenberg BS, Duvoisin RC. Prevalence
and natural history of progressive supranuclear palsy. Neurology
66. Wenning GK, Tison F, Ben-Shlomo Y, Daniel SE, Quinn N.
Multiple system atrophy: a review of 203 pathologically proven
cases. Mov Disord 12:133–147, 1997.
67. Krack P, Pollak P, Limousin P, Hoffmann D, Benazzouz A, Le
Bas JF, et al. Opposite motor effects of pallidal stimulation in
Parkinson’s disease. Ann Neurol 43:180–192, 1998.
68. Litvan I, Chase TN, eds. Traditional and experimental therapeutic
approaches. New York: Oxford University Press, 1992.
69. Wenning GK, Pramstaller PP, Ransmayr G, Poewe W. [A typical
Parkinson syndrome.] Nervenarzt 68:102–115, 1997.
70. Tarsy D, Apetauerova D, Ryan P, Norregaard T. Adverse effects
of subthalamic nucleus DBS in a patient with multiple system
atrophy. Neurology 61:247–249, 2003.
71. Visser-Vandewalle V, Temel Y, Colle H, van der Linden C.
Bilateral high-frequency stimulation of the subthalamic nucleus
in patients with multiple system atrophy—parkinsonism. Report
of four cases. J Neurosurg 98:882–887, 2003.
72. Lezcano E, Gomez-Esteban JC, Zarranz JJ, Alcaraz R, Atares B,
Bilbao G, et al. Parkinson’s disease-like presentation of multiple
system atrophy with poor response to STN stimulation: a clini-
copathological case report. Mov Disord 19:973–977, 2004.
73. Schwarz J, Linke R, Kerner M, Mozley PD, Trenkwalder C,
Gasser T, et al. Striatal dopamine transporter binding assessed by
[I-123]IPT and single photon emission computed tomography in
patients with early Parkinson’s disease: implications for a pre-
clinical diagnosis. Arch Neurol 57:205–208, 2000.
74. Piccini P, de Yebenez J, Lees AJ, Ceravolo R, Turjanski N,
Pramstaller P, et al. Familial progressive supranuclear palsy: de-
tection of subclinical cases using 18F-dopa and 18fluorodeoxy-
glucose positron emission tomography. Arch Neurol 58:1846–
75. Dhawan V, Ma Y, Pillai V, Spetsieris P, Chaly T, Belakhlef A, et
al. Comparative analysis of striatal FDOPA uptake in Parkinson’s
disease: ratio method versus graphical approach. J Nucl Med
76. Ma Y, Dhawan V, Mentis M, Chaly T, Spetsieris PG, Eidelberg
D. Parametric mapping of [18F]FPCIT binding in early stage
Parkinson’s disease: a PET study. Synapse 45:125–133, 2002.
77. Antonini A, Vontobel P, Psylla M, Gunther I, Maguire PR, Mis-
simer J, et al. Complementary positron emission tomographic
studies of the striatal dopaminergic system in Parkinson’s disease.
Arch Neurol 52:1183–1190, 1995.
78. Antonini A, Leenders KL, Vontobel P, Maguire RP, Missimer J,
Psylla M, et al. Complementary PET studies of striatal neuronal
function in the differential diagnosis between multiple system
atrophy and Parkinson’s disease. Brain 120:2187–2195, 1997.
79. Ghaemi M, Hilker R, Rudolf J, Sobesky J, Heiss WD. Differen-
tiating multiple system atrophy from Parkinson’s disease: contri-
bution of striatal and midbrain MRI volumetry and multi-tracer
PET imaging. J Neurol Neurosurg Psychiatry 73:517–523, 2002.
80. Braune S, Reinhardt M, Schnitzer R, Riedel A, Lucking CH.
Cardiac uptake of [123I]MIBG separates Parkinson’s disease
from multiple system atrophy. Neurology 53:1020–1025, 1999.
81. Druschky A, Hilz MJ, Platsch G, Radespiel-Troger M, Druschky
K, Kuwert T, et al. Differentiation of Parkinson’s disease and
multiple system atrophy in early disease stages by means of
I-123-MIBG-SPECT. J Neurol Sci 175:3–12, 2000.
82. Schrag A, Good CD, Miszkiel K, Morris HR, Mathias CJ, Lees
AJ, et al. Differentiation of atypical parkinsonian syndromes with
routine MRI. Neurology 54:697–702, 2000.
83. Seppi K, Schocke MF, Esterhammer R, Kremser C, Brenneis C,
Mueller J, et al. Diffusion-weighted imaging discriminates pro-
gressive supranuclear palsy from PD, but not from the parkinson
variant of multiple system atrophy. Neurology 60:922–927, 2003.
84. Eckert T, Sailer M, Kaufmann J, Schrader C, Peschel T, Bodam-
mer N, et al. Differentiation of idiopathic Parkinson’s disease,
multiple system atrophy, progressive supranuclear palsy, and
healthy controls using magnetization transfer imaging. Neuroim-
age 21:229–235, 2004.
85. Martin WR, Beckman JH, Calne DB, Adam MJ, Harrop R, Rog-
ers JG, et al. Cerebral glucose metabolism in Parkinson’s disease.
Can J Neurol Sci 11:169–173, 1984.
86. Wolfson LI, Leenders KL, Brown LL, Jones T. Alterations of
regional cerebral blood flow and oxygen metabolism in Parkin-
son’s disease. Neurology 35:1399–1405, 1985.
87. Gilman S, Markel DS, Koeppe RA, Junck L, Kluin KJ, Gebarski
SS, et al. Cerebellar and brainstem hypometabolism in olivopon-
tocerebellar atrophy detected with positron emission tomography.
Ann Neurol 23:223–230, 1988.
88. De Volder AG, Francart J, Laterre C, Dooms G, Bol A, Michel C,
et al. Decreased glucose utilization in the striatum and frontal
ECKERT AND EIDELBERG370
NeuroRx?, Vol. 2, No. 2, 2005
lobe in probable striatonigral degeneration. Ann Neurol 26:239– Download full-text
89. Eidelberg D, Takikawa S, Moeller JR, Dhawan V, Redington K,
Chaly T, et al. Striatal hypometabolism distinguishes striatonigral
degeneration from Parkinson’s disease. Ann Neurol 33:518–527,
90. Otsuka M, Ichiya Y, Kuwabara Y, Hosokawa S, Sasaki M, Yo-
shida T, et al. Glucose metabolism in the cortical and subcortical
brain structures in multiple system atrophy and Parkinson’s dis-
ease: a positron emission tomographic study. J Neurol Sci 144:
91. Antonini A, Kazumata K, Feigin A, Mandel F, Dhawan V, Mar-
gouleff C, et al. Differential diagnosis of parkinsonism with
[18F]fluorodeoxyglucose and PET. Mov Disord 13:268–274,
92. Taniwaki T, Nakagawa M, Yamada T, Yoshida T, Ohyagi Y,
Sasaki M, et al. Cerebral metabolic changes in early multiple
system atrophy: a PET study. J Neurol Sci 200:79–84, 2002.
93. Foster NL, Gilman S, Berent S, Morin EM, Brown MB, Koeppe
RA. Cerebral hypometabolism in progressive supranuclear palsy
studied with positron emission tomography. Ann Neurol 24:399–
94. Leenders KL, Frackowiak RS, Lees AJ. Steele-Richardson-Ol-
szewski syndrome. Brain energy metabolism, blood flow and
fluorodopa uptake measured by positron emission tomography.
Brain 111:615–630, 1988.
95. Blin J, Baron JC, Dubois B, Pillon B, Cambon H, Cambier J, et
al. Positron emission tomography study in progressive supranu-
clear palsy. Brain hypometabolic pattern and clinicometabolic
correlations. Arch Neurol 47:747–752, 1990.
96. Blin J, Vidailhet MJ, Pillon B, Dubois B, Feve JR, Agid Y.
Corticobasal degeneration: decreased and asymmetrical glucose
consumption as studied with PET. Mov Disord 7:348–354, 1992.
97. Eidelberg D, Dhawan V, Moeller JR, Sidtis JJ, Ginos JZ, Strother
SC, et al. The metabolic landscape of cortico-basal ganglionic
degeneration: regional asymmetries studied with positron emis-
sion tomography. J Neurol Neurosurg Psychiatry 54:856–862,
98. Laureys S, Salmon E, Garraux G, Peigneux P, Lemaire C,
Degueldre C, et al. Fluorodopa uptake and glucose metabolism in
early stages of corticobasal degeneration. J Neurol 246:1151–
99. Eckert T, Barnes A, Frucht S, Dhawan V, Feigin A, Eidelberg D.
Differential diagnosis of parkinsonian disorders: the diagnostic
value of FDG PET. Mov Disord 19:S376, 2004.
100. Feigin A, Ma Y, Zgaljardic D, Carbon M, Dhawan V, Eidelberg
D. PET measures of longitudinal progression in presymptomatic
Huntington’s disease. Neurology 60(Suppl 1):A246, 2003.
101. Feigin A, Budman C, Zgaljardic D, Dhawan V, Eidelberg D.
Metabolic brain networks in Tourette syndrome. Mov Disord
17(Suppl 5):S339, 2002.
102. Pavese N, Andrews TC, Brooks DJ, Ho AK, Rosser AE, Barker
RA, et al. Progressive striatal and cortical dopamine receptor
dysfunction in Huntington’s disease: a PET study. Brain 126:
103. Aylward EH, Sparks BF, Field KM, Yallapragada V, Shpritz BD,
Rosenblatt A, et al. Onset and rate of striatal atrophy in preclinical
Huntington disease. Neurology 63:66–72, 2004.
104. Antonini A, Leenders KL, Spiegel R, Meier D, Vontobel P,
Weigell-Weber M, et al. Striatal glucose metabolism and dopa-
mine D2 receptor binding in asymptomatic gene carriers and
patients with Huntington’s disease. Brain 119:2085–2095, 1996.
105. Klein C, Breakefield XO, Ozelius LJ. Genetics of primary dys-
tonia. Semin Neurol 19:271–280, 1999.
106. Eidelberg D, Moeller JR, Ishikawa T, Dhawan V, Spetsieris P,
Przedborski S, et al. The metabolic topography of idiopathic
torsion dystonia. Brain 118:1473–1484, 1995.
107. Eidelberg D. Brain networks and clinical penetrance: lessons
from hyperkinetic movement disorders. Curr Opin Neurol 16:
108. Ghilardi MF, Carbon M, Silvestri G, Dhawan V, Tagliati M,
Bressman S, et al. Impaired sequence learning in carriers of the
DYT1 dystonia mutation. Ann Neurol 54:102–109, 2003.
109. Bressman SB. Dystonia genotypes, phenotypes, and classifica-
tion. Adv Neurol 94:101–107, 2004.
110. Carbon M, Kingsley PB, Su S, Smith GS, Spetsieris P, Bressman
S, et al. Microstructural white matter changes in carriers of the
DYT1 gene mutation. Ann Neurol 56:283–286, 2004.
111. Carbon M, Su S, Dhawan V, Raymond D, Bressman S, Eidelberg
D. Regional metabolism in primary torsion dystonia: effects of
penetrance and genotype. Neurology 62:1384–1390, 2004.
112. Jenkins IH, Fernandez W, Playford ED, Lees AJ, Frackowiak RS,
Passingham RE, et al. Impaired activation of the supplementary
motor area in Parkinson’s disease is reversed when akinesia is
treated with apomorphine. Ann Neurol 32:749–757, 1992.
113. Feigin A, Ghilardi MF, Fukuda M, Mentis MJ, Dhawan V, Bar-
nes A, et al. Effects of levodopa infusion on motor activation
responses in Parkinson’s disease. Neurology 59:220–226, 2002.
114. Haslinger B, Erhard P, Kampfe N, Boecker H, Rummeny E,
Schwaiger M, et al. Event-related functional magnetic resonance
imaging in Parkinson’s disease before and after levodopa. Brain
115. Fukuda M, Mentis M, Ghilardi MF, Dhawan V, Antonini A,
Hammerstad J, et al. Functional correlates of pallidal stimulation
for Parkinson’s disease. Ann Neurol 49:155–164, 2001.
116. Ceballos-Baumann AO. Functional imaging in Parkinson’s dis-
ease: activation studies with PET, fMRI and SPECT. J Neurol
117. Thobois S, Jahanshahi M, Pinto S, Frackowiak R, Limousin-
Dowsey P. PET and SPECT functional imaging studies in Par-
kinsonian syndromes: from the lesion to its consequences. Neu-
roimage 23:1–16, 2004.
118. Sadato N, Ibanez V, Deiber MP, Campbell G, Leonardo M,
Hallett M. Frequency-dependent changes of regional cerebral
blood flow during finger movements. J Cereb Blood Flow Metab
119. Dai TH, Liu JZ, Sahgal V, Brown RW, Yue GH. Relationship
between muscle output and functional MRI-measured brain acti-
vation. Exp Brain Res 140:290–300, 2001.
120. Mentis MJ, Dhawan V, Nakamura T, Ghilardi MF, Feigin A,
Edwards C, et al. Enhancement of brain activation during trial-
and-error sequence learning in early PD. Neurology 60:612–619,
121. Fukuda M, Ghilardi MF, Carbon M, Dhawan V, Ma Y, Feigin A,
et al. Pallidal stimulation for parkinsonism: improved brain acti-
vation during sequence learning. Ann Neurol 52:144–152, 2002.
122. Feigin A, Ghilardi MF, Carbon M, Edwards C, Fukuda M, Dha-
wan V, et al. Effects of levodopa on motor sequence learning in
Parkinson’s disease. Neurology 60:1744–1749, 2003.
123. Carbon M, Ghilardi MF, Feigin A, Fukuda M, Silvestri G, Mentis
MJ, et al. Learning networks in health and Parkinson’s disease:
reproducibility and treatment effects. Hum Brain Mapp 19:197–
DB, Krueger G, Moseley ME, Glover GH. Foundations of advanced
magnetic resonance imaging. NeuroRx 2:167–196, 2005.
NEUROIMAGING, THERAPEUTICS IN MOVEMENT DISORDERS371
NeuroRx?, Vol. 2, No. 2, 2005