Abnormal metabolic brain networks in Parkinson’s disease

SECTION II

Exploring PD with brain imaging

A. Bjorklund and M. A. Cenci (Eds.)

Progress in Brain Research, Vol. 184

ISSN: 0079-6123

Copyright
2010 Elsevier B.V. All rights reserved.

CHAPTER 8

Abnormal metabolic brain networks in Parkinson’s disease: from blackboard to bedside Chris C. Tang† and David Eidelberg
,†,‡ †

Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY, USA ‡ Departments of Neurology and Medicine, North Shore University Hospital, Manhasset, NY, USA

Abstract: Metabolic imaging in the rest state has provided valuable information concerning the abnormalities of regional brain function that underlie idiopathic Parkinson’s disease (PD). Moreover, network modeling procedures, such as spatial covariance analysis, have further allowed for the quantification of these changes at the systems level. In recent years, we have utilized this strategy to identify and validate three discrete metabolic networks in PD associated with the motor and cognitive manifestations of the disease. In this chapter, we will review and compare the specific functional topographies underlying parkinsonian akinesia/rigidity, tremor, and cognitive disturbance. While network activity progressed over time, the rate of change for each pattern was distinctive and paralleled the development of the corresponding clinical symptoms in early-stage patients. This approach is already showing great promise in identifying individuals with prodromal manifestations of PD and in assessing the rate of progression before clinical onset. Network modulation was found to correlate with the clinical effects of dopaminergic treatment and surgical interventions, such as subthalamic nucleus (STN) deep brain stimulation (DBS) and gene therapy. Abnormal metabolic networks have also been identified for atypical parkinsonian syndromes, such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Using multiple disease-related networks for PD, MSA, and PSP, we have developed a novel, fully automated algorithm for accurate classification at the single-patient level, even at early disease stages. Keywords: brain networks; glucose metabolism; Parkinson’s disease; differential diagnosis




Corresponding author. Tel: þ1-516-5622498; Fax: þ1-516-5621008; E-mail: [email protected]

DOI: 10.1016/S0079-6123(10)84008-7

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Abnormal metabolic networks in Parkinson’s disease Parkinson’s disease (PD) is characterized by the insidious onset and inexorable progression of both motor and cognitive symptoms. Prodromal symp­ toms such as hyposmia or rapid eye movement (REM) behavior disorder might predate the appearance of the classic motor symptoms by years. Characteristic motor manifestations of PD typically appear only after more than 50% of nigrostriatal dopaminergic neurons have been lost (Bernheimer et al., 1973). Thus far there is a paucity of reliable biomarkers to identify preclini­ cal PD or monitor disease progression through its natural history or response to treatment. Functional positron emission tomography (PET) imaging methods have proven useful in fill­ ing this void, particularly at the system-wide level. Spatial covariance analysis of metabolic imaging data acquired with [18F]-fluorodeoxyglucose (FDG) PET has become an important means of detecting network-level functional abnormalities in neurodegenerative disorders such as PD, Huntington’s disease, and Alzheimer’s disease. The details of this approach have been summar­ ized elsewhere (see Eidelberg, 2009 for review). In brief, spatial covariance mapping utilizes principal component analysis (PCA), a multivariate method designed to isolate linearly independent sources of variability in large datasets. In typical multisubject, multi-voxel metabolic imaging data, this approach is applied to a combined sample of scans from patients and healthy subjects to identify one or more spatial covariance patterns that differenti­ ate between the two groups (e.g., Feigin et al., 2007b; Habeck et al., 2008; Ma et al., 2007). The expression of a given disease-related metabolic pattern can be quantified prospectively on an indi­ vidual scan basis through the operation of dot product computation. The resulting subject scores (i.e., pattern expression values) can be used in further investigations of group discrimination, dis­ ease progression, treatment effects, or correlations with independent clinical or physiological indices.

In this chapter we will briefly describe recent advances in imaging analysis and the potential ramifications of this approach on the investigation of PD and related neurodegenerative disorders.

The PD-related motor pattern (PDRP) We have recently identified and validated several PD-related spatial covariance patterns involving metabolic changes at key nodes of the cortico­ striato-pallido-thalamocortical (CSPTC) loops and related anatomical/functional pathways (see Eidelberg, 2009; Hirano et al., 2009; Poston and Eidelberg, 2009 for review). By applying network analysis to FDG PET data in the rest state, we have found that the activity of an abnormal meta­ bolic network is elevated in PD patients relative to healthy control subjects (Ma et al., 2007). This pattern is associated with the motor manifesta­ tions of the disease and is characterized by covarying increases in pallido-thalamic and pon­ tine metabolic activity and relative reductions in the premotor cortex, supplementary motor area (SMA), and parietal association areas (Fig. 1a). To date, we have verified the presence of this abnormal PD-related motor pattern (PDRP) in seven independent patient populations scanned under widely different rest-state imaging proto­ cols (Eidelberg, 2009). We have additionally demonstrated that quantitative measures of PDRP expression are highly reproducible in indi­ vidual patients undergoing repeat imaging proce­ dures (Ma et al., 2007). It is important to note that not only does PDRP expression accurately discriminate between PD patients and healthy controls, but individual sub­ ject scores consistently correlate with composite Unified Parkinson’s Disease Rating Scale (UPDRS) motor ratings in different PD popula­ tions (Asanuma et al., 2006; Eidelberg et al., 1994, 1995; Feigin et al., 2002, Lozza et al., 2004) (Fig. 1b). In particular, abnormal elevations in PDRP network activity have been linked to the akinetic-rigid manifestations of the disease but

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not to tremor (Antonini et al., 1998; Isaias et al., 2010, cf. Eidelberg et al., 1990, 1994, 1995). Parkinsonian tremor has recently been found to be associated with a discrete spatial covariance pattern, independent of the PDRP topography. Specifically, in a recent study, we identified a candidate PD tremor-related metabolic pattern (PDTP) using a novel within-subjects network mapping approach (Habeck and Stern, 2007). Using supervised principal components analysis (PCA), we detected a highly significant metabolic pattern with consistently lower expression during ventro-intermediate (Vim) thalamic nucleus sti­ mulation (“on”) relative to baseline (“off”). The expression of this pattern, which was characterized by metabolic increases in the cerebellum/dorsal pons, caudate/putamen, and primary motor cortex (Fig. 1c), correlated with tremor severity (Fig. 1d) but not with akinesia/rigidity measures. PDTP expression measured in PD patients scanned with 99m Tc-ECD single photon emission computed tomography (SPECT) perfusion imaging (Isaias et al., 2010) was selectively elevated in tremordominant PD patients as compared to their aki­ netic-rigid counterparts and to healthy control subjects. As will be described below, elevations in PDRP expression are specific for idiopathic PD and can be used to differentiate this condition from atypi­ cal parkinsonian syndromes (Tang et al., 2010b). Moreover, the activity of this network may pre­ cede the onset of motor symptoms by several years (Tang et al., 2010a). That said, other PDrelated metabolic networks can be expressed as part of the natural history of this disorder.

The PD-related cognitive pattern (PDCP) Cognitive deficits and behavioral abnormalities are also well documented in PD and can have a major impact on quality of life (Aarsland et al., 2005; Schrag et al., 2000), but the pathological basis of cognitive impairment in PD remains con­ troversial (Emre, 2003). Ample evidence exists for

Alzheimer’s disease (AD)-type changes in cogni­ tively impaired PD patients (Jellinger et al., 2002). More recent studies utilizing a-synuclein (aSN) immunostaining have demonstrated that cortical Lewy body pathology is likely to be the most critical feature of this clinical syndrome (Braak et al., 2003; Hurtig et al., 2000). Indeed, the minimental status examination, a coarse description of cognitive status, correlates with the magnitude and distribution of Lewy body pathology at post­ mortem examination (Braak et al., 2005). Metabolic imaging in the resting state has proved useful in investigating the basis for impaired cognitive functioning in PD. Using reststate FDG PET and network analysis, we identi­ fied a distinct cognition-related spatial covariance pattern in non-demented PD patients (Huang et al., 2007a). This PD-related cognitive pattern (PDCP) is characterized by covarying reductions in metabolic activity involving the rostral supple­ mentary motor area (pre-SMA), prefrontal cor­ tex, precuneus, and parietal association regions, with relative increases in the cerebellar vermis and dentate nuclei (Fig. 1e). Quantitative mea­ sures of PDCP expression have been found to correlate with subject performance on neuropsy­ chological tests of executive functioning such as the California and Hopkins Verbal Learning Tests (CVLT, HVLT), Trails B, and Stroop (color) tests (Fig. 1f) (Huang et al., 2007a). Like PDRP scores, PDCP expression exhibits excellent test–retest reproducibility in patients undergoing repeat FDG PET over an 8-week period. Moreover, by computing PDCP expression in a prospective group of non-demented PD patients with and without minimal cognitive impairment (MCI) on neuropsychological testing, we found that PDCP scores were higher in the PD patients with MCI than in those who were cognitively intact (Huang et al., 2008, see Eidelberg, 2009 for review). The relationship between abnormal PDCP expression in the resting state and changes in brain deactiva­ tion during the performance of cognitive tasks (Argyelan et al., 2008) is a topic of ongoing investigation.

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Network activity evolves with disease progression The prolonged asymptomatic state in PD patients with extensive brainstem Lewy body pathology suggests that the brain can summon effective com­ pensatory mechanisms for quite some time (Bezard et al., 2003; Smith and Zigmond, 2003). That said, the functional changes in CSPTC loops

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and related pathways that develop as a conse­ quence of pre-symptomatic loss of SNc neurons are not well understood (e.g., Buhmann et al., 2003; Hirano et al., 2008, cf. Moeller and Eidel­ berg, 1997), and little is known about metabolic changes associated with the onset of clinical symp­ toms of this disease. To approach these questions, we made use of the fact that PD typically presents

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unilaterally (i.e., hemiparkinsonism) to assess metabolic changes in the brain hemisphere con­ tralateral to the unaffected side. In a recent study (Tang et al., 2010a), we assessed hemispheric progression in a cohort of 15 hemiparkinsonian patients undergoing serial imaging with FDG PET at baseline and again after 2- and 4-years’ follow-up. We separately quantified the activity of the PDRP and PDCP metabolic networks in the cerebral hemispheres ipsilateral and contralateral to the initial clinical signs at each of the three longitudinal time points. These measurements allowed for the determina­ tion of the time course of network progression in each hemisphere, and on the ipsilateral side, the specific metabolic changes associated with symp­ tom onset. Contrary to expectation, we found sig­ nificant baseline elevations in PDRP expression on the ipsilateral (“preclinical”) side (Fig. 2a), preceding the appearance of motor signs on the opposite body side by approximately 2 years. By contrast, expression of the PDCP network did not

reach abnormal levels until the last time point (Fig. 2b), which was approximately 4 years after PDRP elevation. Notably, significant PDCP eleva­ tions were evident in both hemispheres several years before the typical onset of mild cognitive impairment (MCI) in PD (Eidelberg, 2009; Huang et al., 2008). Furthermore, examination of whole-brain net­ work activity in this longitudinal cohort demon­ strated that PDRP expression increases linearly over time (Fig. 2c) and is accompanied by com­ mensurate increases in UPDRS motor ratings. PDCP expression also increases over time, but at a significantly slower rate than for PDRP scores. Interestingly, the longitudinal changes in PDTP expression are yet more gradual, corresponding to the relatively slower rate of progression docu­ mented for tremor in PD (Louis et al., 1999). The three PD-related metabolic networks (PDRP, PDCP, and PDTP) thus appear to capture unique clinical and mechanistic features of the disease process.

Fig. 1. Abnormal metabolic networks in Parkinson’s disease. (a) PD-related motor pattern (PDRP) identified by spatial covariance analysis of [18F]-fluorodeoxyglucose (FDG) PET scans from 33 PD patients and 33 age-matched normal volunteer subjects. This pattern is characterized by relative hypermetabolism (red) in the globus pallidus/putamen (GP/Put), thalamus, pons, cerebellum, and sensorimotor cortex, associated with metabolic decreases (blue) in the lateral premotor cortex (PMC) and parieto-occipital association regions (Ma et al., 2007). Representative slices of the covariance map were overlaid on a standard MRI brain template. (b) PDRP expression correlated with composite Unified Parkinson’s Disease Rating Scale (UPDRS) motor ratings in each of the three independent, prospectively imaged patient groups (circles: n = 27; r = 0.66; p < 0.001; squares: n = 15; r = 0.65, p < 0.01; triangles: n = 23; r = 0.76, p < 0.001), as well as in the combined cohort (n = 65; r = 0.68, p < 0.001). In each group, PDRP scores correlated with subscale ratings for akinesia/rigidity but not with tremor ratings (Asanuma et al., 2006, Lozza et al., 2004, Eidelberg et al., 1995). (c) PD-related tremor pattern (PDTP) identified by supervised principal components analysis (PCA) (Habeck and Stern, 2007) of FDG PET scans from nine tremor-predominant PD patients scanned at baseline and during high-frequency deep brain stimulation (DBS) of the ventralintermediate (Vim) thalamic nucleus. This pattern is characterized by relative hypermetabolism of sensorimotor cortex (SMC), cerebellum, pons, and putamen. Representative slices of the covariance map were overlaid on a standard MRI brain template. (d) PDTP expression correlated with UPDRS tremor ratings in a prospective group of PD patients (n = 35; r = 0.53, p = 0.001). (e) PD-related cognitive pattern (PDCP) identified by spatial covariance analysis of FDG PET scans from 15 non-demented PD patients with mild-to­ moderate motor symptoms. This pattern is characterized by relative hypometabolism (blue) in the rostral supplementary motor area (preSMA), precuneus, premotor cortex (PMC), posterior parietal and prefrontal regions, associated with metabolic increases (red) in the cerebellar/dentate nucleus (DN) (Huang et al., 2007a). Representative slices of the covariance map were overlaid on a standard MRI brain template. (f) PDCP expression correlated with performance on California Verbal Learning Test (sum) in the original group for pattern derivation (circles; n = 15: r = 0.71, p < 0.005) and in each of the two prospective validation groups (squares; n = 25: r = 0.53, p < 0.01; triangles; n = 16: r = 0.80, p < 0.001) (Huang et al., 2007a). The correlation was also significant in the combined cohort (n = 56; r = 0.67, p < 0.001). Subject scores for each of the three networks were z-transformed so that the normal mean is zero and standard deviation is 1. [a, b, e, and f: Reprinted from Trends Neurosci, Metabolic brain networks in neurodegenerative disorders: a functional imaging approach, 548–557, Copyright 2009, with permission from Elsevier; c and d: courtesy of Dr. H. Mure]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this book.)

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Fig. 2. Changes in network activity with disease progression. (a) Mean PDRP expression in the hemispheres contralateral (circles) and ipsilateral (triangles) to the initially affected limbs in 15 hemiparkinsonian patients who underwent FDG PET at baseline, 2, and 4 years (Tang et al., 2010a). Relative to healthy controls, PDRP expression in the PD patients was abnormally elevated (p < 0.05) in both hemispheres relative to controls at baseline when motor symptoms only appeared on one side of the body; network activity continued to increase in parallel over the course of the study (p < 0.001). (b) By contrast, mean PDCP expression reached abnormally elevated levels (p < 0.01) in both the contralateral (circles) and ipsilateral (triangles) hemispheres only at the final time point. In both hemispheres, PDCP network activity increased in parallel over time (p < 0.005). For both PDRP and PDCP, subject scores in each hemisphere were z-transformed so that the normal mean is zero and standard deviation is 1. (c) Mean activity of the PD-related motor (PDRP), cognitive (PDCP), and tremor (PDTP) spatial covariance patterns at baseline, 2, and 4 years. Network activity increased significantly over time for all three patterns (PDRP: p < 0.0001; PDCP: p < 0.0001; PDTP: p = 0.01), but at different rates (p < 0.01). Of the three patterns, PDRP expression progressed most rapidly while PDTP progression was the slowest, corresponding to the concurrent clinical changes observed in this cohort. Subject scores for each of the three networks were z-transformed so that the normal mean is zero and standard deviation is 1. [a and b: Adapted from J Neurosci, Abnormalities in metabolic network activity precede the onset of motor symptoms in Parkinson’s disease, 1049–1056, Copyright 2010, with permission from the Society for Neuroscience. PDTP, courtesy of Dr. H. Mure].

Assessing treatment effects with network activity Dopaminergic treatment Quantitative imaging measures such as metabolic network activity can also be valuable for the objective assessment of treatment efficacy. To qualify as treatment biomarkers in clinical trials, metabolic networks should exhibit consistent change with therapeutic interventions, ideally at the individual subject level. This attribute has been demonstrated for PDRP expression, in that treatment-mediated changes in network activity have been shown to correlate with clinical improvement in UPDRS motor ratings in patients undergoing dopaminergic treatment (Feigin et al., 2001), deep brain stimulation (Asanuma et al., 2006; Fukuda et al., 2001), and gene therapy (Feigin et al., 2007a) (Fig. 3a).

In general, network expression values derived from metabolic scans such as FDG PET correlate closely with those derived from measures of cere­ bral blood flow (H15 2 O PET, arterial spin-labeled MRI) in the same subjects. It is thus particularly interesting that patients on levodopa/carbidopa–– but not other therapies, including DBS––show a significant dissociation between changes in PDRP expression measured in cerebral metabolism scans and that measured in cerebral blood flow scans (Hirano et al., 2008). Specifically, patients receiv­ ing levodopa/carbidopa show reductions in PDRP expression in the former (FDG PET), but increases in the latter (H15 2 O PET) following acute treatment. Notably, levodopa-mediated dissociation of cerebral blood flow and metabolism was found to be greatest in PD patients with levo­ dopa-induced dyskinesias (LID). We propose that the observed flow–metabolism dissociation results

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Fig. 3. Assessment of treatment effects with network activity. (a) Treatment-mediated changes in mean PDRP expression. Left panel shows network modulations after the levodopa infusion (LD, gray), the bilateral deep brain stimulation of the subthalamic nucleus (STNBi DBS, black), and for the test–retest PD controls (CN, white) (Asanuma et al., 2006). Right panel shows network modulations after the unilateral DBS (filled black) or lesioning (stripe black) of the internal globus pallidus (GPi) or STN (Fukuda et al., 2001; Trost et al., 2006). Error bar represents the SEM.
p < 0.05,

p < 0.01. (Adapted from Brain, Network modulation in the treatment of Parkinson’s disease, 2667–2678, Copyright 2006, with permission from Oxford University Press.) (b) Changes in mean PDRP network activity over time for the operated (filled circles) and unoperated (open circles) hemispheres after gene therapy. There was a significant difference (p < 0.005) in the time course of PDRP activity across the two hemispheres. In the unoperated hemisphere, network activity increased continuously over the 12 months following surgery. By contrast, in the operated hemisphere, network activity declined during the first 6 months and then increased in parallel with values on the unoperated side over the subsequent 6 months. The dashed line represents one standard error above the normal mean value of 0. (c) By contrast, there was no change (p = 0.72) in PDCP network activity in either of the two hemispheres over time. The dashed line represents one standard error above the normal mean value of zero. [b and c: Reprinted from PNAS, Modulation of metabolic brain networks after subthalamic gene therapy for Parkinson’s disease, 19559–19564, Copyright 2007, with permission from the National Academy of Science]. Subject scores for each network were z-transformed so that the normal mean is 0 and standard deviation is 1.

from neurovascular alterations (i.e., dopaminergic vasodilation) that likely underlie LID in PD patients. Further study is needed to understand the cause of LID, whether flow–metabolism dissociation occurs with other forms of dopaminergic

therapy, and whether this side effect can be miti­ gated by antidyskinetic agents. Levodopa has been found not to modulate PDCP expression at the group mean level in PD patient populations (Huang et al., 2007a). However, by

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analogy to our observations in PD patients scanned while learning motor sequences (Argyelan et al., 2008), PDCP modulation is likely to be baseline dependent. Indeed, preliminary evidence suggests that cognition-related resting-state cerebral func­ tion at both the regional and network levels can be pharmacologically modulated based upon the extent of the metabolic abnormality present in the unmedicated condition. Currently, prospective stu­ dies are underway to investigate the effect of base­ line PDCP expression on the cognitive response to medication on an individual patient basis.

Deep brain stimulation and microlesion effect Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has proven to be highly effective for advanced PD motor symptoms (Benabid et al., 2009). Indeed, DBS interventions at STN and internal globus pallidus (GPi) have been shown to modulate the activity of the PDRP metabolic network (Fig. 3a), with significant correlations between reductions in pattern expression and clin­ ical improvement in motor function (Asanuma et al., 2006; Fukuda et al., 2001; Trost et al., 2006). Notably, there is an association between PDRP expression and spontaneous firing rates recorded during stereotaxic surgery (Lin et al., 2008, cf. Eidelberg et al., 1997). This likely reflects the pathophysiological basis of this disease-related motor network abnormality (Eberling et al., 2008). It is worth noting that the magnitude of PDRP modulation is comparable for STN DBS and levodopa treatments but that combining the two therapies confers no additional benefit. This is consistent with the notion that the two interven­ tions exert their therapeutic benefits through the same mechanistic pathway. It is also worth noting that electrode implanta­ tion itself, without stimulation, can induce a microlesion effect on pallido-thalamic brain function (Pourfar et al., 2009) analogous to that seen following therapeutic STN lesioning (subthala­ motomy) (Trost et al., 2003, 2006). However, the

magnitude of this highly localized metabolic change is not strong enough to produce consistent changes in PDRP expression or significant clinical benefit (Pourfar et al., 2009). These data suggest that a minimum threshold for PDRP modulation exists and is necessary for a positive outcome to occur following treatment. Moreover, treatment effects on network activity appear to be highly selective. For instance, we have recently noted that high-frequency stimula­ tion of the Vim thalamic nucleus, while highly effective for parkinsonian tremor, had little effect on akinesia or rigidity. Accordingly, Vim DBS was found to have a significant effect on PDTP but not PDRP activity (H. Mure, personal communica­ tion). STN DBS, by contrast, improved both PD tremor and akinesia/rigidity and was associated with substantial reductions in the activity of both metabolic networks.

Gene therapy Animal studies suggested that transfer of the glu­ tamic acid decarboxylase (GAD) gene into the STN can suppress spontaneous neural activity in this region and increase GABA release in down­ stream areas (Lee et al., 2005; Luo et al., 2002), leading to improvement in parkinsonian motor manifestations (Emborg et al., 2007). In a subse­ quent Phase I clinical trial, adeno-associated virus (AAV) was used to deliver the GAD gene uni­ laterally into the STN of advanced PD patients (Kaplitt et al., 2007). Each subject underwent clin­ ical evaluation and FDG PET at three time points: before surgery, then at 6 and 12 months after surgery (Feigin et al., 2007a). At baseline, hemi­ spheric PDRP expression was elevated bilaterally. Following gene transfer, this network abnormality was suppressed on the treated side (Fig. 3b), with concomitant improvement in contralateral limb motor ratings. In the unoperated hemisphere, however, network activity increased over time following surgery, consistent with disease progres­ sion (cf. Huang et al., 2007b). Gene therapy did

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not alter PDCP expression in either hemisphere (Fig. 3c), in accordance with the absence of cogni­ tive change in these patients. Given these findings, a blinded sham-surgery controlled Phase II study of bilateral STN AAV-GAD gene therapy is cur­ rently underway for advanced PD motor symp­ toms. FDG PET studies are being conducted under the blind, with results to become available in the latter half of 2010. Together, the findings of these studies suggest that abnormal metabolic networks can be used as imaging biomarkers for assessing clinically mean­ ingful treatment effects as well as for understand­ ing the pathophysiological mechanisms underlying these therapies. Network analysis may also be useful in evaluating novel treatments in clinical trials for PD.

Differential diagnosis of parkinsonian conditions The challenge of early parkinsonian symptoms The classic parkinsonian clinical triad of rigidity, resting tremor, and bradykinesia is not limited to PD; other atypical parkinsonian syndromes (APSs), such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), can produce very similar clinical signs especially in the early stages when signs are mild. Pathological studies indicate that up to 25% of patients clini­ cally diagnosed to have PD actually have a differ­ ent disease; approximately 80% of misdiagnoses turn out to be MSA and PSP (Hughes et al., 2001a, 2002). Conversely, clinically misdiagnosed MSA and PSP cases are often found to display Lewy body changes at postmortem (Osaki et al., 2002, 2004). Clinicopathologic studies of patients with par­ kinsonism have found that the positive predictive value (PPV) for an initial clinical diagnosis of PD can be as low as 75%, although the PPV improves drastically to 98.6% after patients are followed over 2 years by movement disorders specialists (Hughes et al., 1992a, 2001b, 2002). While strict

diagnostic guidelines have improved the PPV for a diagnosis of MSA or PSP, the sensitivity for these diagnoses at initial clinical visit with a movement disorder specialist remains low (<70%) for both disorders (Osaki et al., 2002, 2004). More problematically, up to 15% of patients enrolled in large clinical trials for early PD can end up with a different diagnosis after long-term clinical follow-up (see e.g., Fahn et al., 2004; Parkinson Study Group, 2002; Whone et al., 2003). It is clear that diagnostic biomarkers to differentiate PD from APS, if validated, will help assure the accuracy of clinical trials to assess disease modification in early-stage PD patients (Tang et al., 2010b). Most neuroimaging techniques for the differen­ tial diagnosis of parkinsonism have not been shown to reliably discriminate between idiopathic PD and APSs in early-stage patients, particularly before the diagnosis is achieved by clinical evalua­ tion. Some imaging techniques, such as SPECT and transcranial sonography (TCS), are able to discriminate patients with parkinsonism from nor­ mal volunteer subjects, or to establish group mean level separation between diagnostic classes––but have not achieved accurate individual diagnosis in de novo patients (see e.g., Doepp et al., 2008; Vlaar et al., 2007). In part, this is because such techniques measure nigrostriatal dopaminergic projections and/or localized structural/functional changes in the basal ganglia, which are also evi­ dent in atypical neurodegenerative syndromes such as MSA and PSP.

Differentiating PD, MSA, and PSP Pattern analysis of metabolic images can provide an unbiased whole-brain evaluation of functional changes in the basal ganglia and in interconnected brain regions. This is potentially a more accurate means for differentiating between parkinsonian syndromes by capturing abnormal metabolic changes in network regions known to be specifi­ cally related to each disease process. As such, we

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hypothesized that pattern-based imaging classifi­ cation can provide accurate differential diagnosis for patients with parkinsonism several years before a final clinical diagnosis is made. First, we identified specific disease-related metabolic patterns for MSA and PSP (termed MSARP and PSPRP, respectively) (Eckert et al., 2008). The MSARP is characterized by bilateral metabolic reductions in the putamen and cerebel­ lum (Fig. 4a, left); the PSPRP is characterized by metabolic decreases predominately in the upper brainstem, medial prefrontal cortex, and medial thalamus (Fig. 4c, left). These patterns accurately discriminate between patients and healthy con­ trols on a prospective single-case basis (Fig. 4b and d) (Eckert et al., 2008; Spetsieris et al., 2009). To determine whether these patterns would prove useful in discriminating between PD, PSP, and MSA in patients with early-stage disease, we recruited and scanned 167 patients with parkin­ sonism who were referred to our center for FDG PET by movement disorders specialists because of uncertain clinical diagnosis (Tang et al., 2010b). After PET, all patients were followed clinically by movement disorders specialists for an average of 2.6 + 2.4 (mean + SD) years before a final clinical diagnosis was reached. For every scan, we com­ puted the expression (i.e., subject scores) of the PDRP, MSARP, and PSPRP separately on an individual scan basis. Utilizing these pattern scores in conjunction with logistic regression mod­ els, we then developed a two-level, automated algorithm to classify the individual subjects between PD, MSA, and PSP. For first-level analysis, we combined the clini­ cally diagnosed MSA and PSP patients into a single APS group. We then employed a logistic regression model to discriminate between the PD and APS groups and to compute the probabilities for having PD and APS for all individuals. These probabilities were used to determine the diagnos­ tic criteria for the image-based classification of PD vs. APS for each subject. For second-level analy­ sis, subjects classified as APS were further ana­ lyzed to differentiate between MSA and PSP.

Logistic regression models were also used for dif­ ferentiation of MSA vs. PSP and for computation of probabilities for these two disorders in each APS subject. Subsequently, these probabilities were used to determine the diagnostic criterion for the image-based classification of MSA vs. PSP for each subject. By comparing the imagebased classification for each patient to the final clinical diagnosis obtained after several years of clinical follow-up, we calculated discriminative measures––sensitivity, specificity, PPV, and nega­ tive predictive value (NPV)––for each of the three disorders. We found that in our patient cohort, the imagebased classification results were highly specific and accurate in discriminating among PD, MSA, and PSP. Imaging classification exhibited 84% sensitivity, 97% specificity, 98% PPV, and 82% NPV for the clinical PD subjects. It exhibited 85% sensitivity, 96% specificity, 97% PPV, and 83% NPV for the MSA subjects, and 88% sensi­ tivity, 94% specificity, 91% PPV, and 92% NPV for the PSP subjects. We further divided the subjects into subgroups according to the duration of their disease at the time of scanning and by the duration of follow-up. The classification results remained excellent (>90% specificity) even in the subgroups of early patients with very short symptom durations (i.e., <2 years), whose clinical diagnoses were sub­ sequently confirmed after more than 2-year followup by movement disorders specialists. Moreover, there was excellent agreement between the imaging classifications obtained in a group of patients who underwent repeat PET imaging separated by an average of 3.1 + 2.2 (SD) years. Imaging classifica­ tion was also confirmed in a small subgroup of subjects at autopsy (Fig. 4a and c, right). The results of this study indicate that our auto­ mated algorithm with FDG PET can accurately differentiate PD, MSA, and PSP in patients with parkinsonism prior to the development of clini­ cally diagnostic symptoms and signs. Our findings support the use of FDG PET and pattern analysis in the identification of patients with PD or APS

A Multiple system atrophy

B

Putamen

2.60 μm

Gallyas

Cerebellum

0.676 μm 1.18 μm

MSARP expression

4 3

p < 0.001

2 1 0 –1 –2

2.60 μm

p < 0.001 p < 0.001

Training Testing Normals

Training Testing A Testing B MSA

Gallyas

C Progressive supranuclear palsy

D

Pons

1.53 μm

Bielschowsky

Frontal cortex

PSPRP expression

5 4

p < 0.001

3 2 1 0 –1 –2

0.56 μm

p < 0.001 p < 0.001

Training Testing Normals

Training Testing A Testing B PSP

AT8

Fig. 4. Abnormal metabolic networks in atypical parkinsonian syndromes and postmortem findings. (a). Multiple system atrophy-related pattern (MSARP; left) identified by spatial covariance analysis of FDG PET scans from 10 MSA patients and 10 healthy controls. This pattern is characterized by covarying metabolic decreases (blue) in the putamen and the cerebellum (Eckert et al., 2008). Representative slices of the covariance map were overlaid on a standard MRI brain template. Neuropathological findings (right) from a patient classified as MSA with a likelihood of 98% by the automated differential diagnosis algorithm based on an FDG PET scan performed 3 years before death (Tang et al., 2010b). Autopsy revealed characteristic changes of neuronal loss and gliosis in the putamen (top) and cerebellum (bottom). Both regions displayed glial cytoplasmic inclusions (Gallyas stain, 200×). Insets show areas of higher magnification (putamen, 400×; cerebellum, 630×). (b) MSARP expression was significantly elevated (p < 0.001) in the training group of 10 MSA patients (open diamonds) relative to the 10 healthy controls (open circles) that were used for pattern derivation. Similarly, pattern expression was elevated (p < 0.001) in the two testing groups of MSA patients (closed diamonds) relative to a testing group of healthy controls (closed circles). Mean and standard deviation are also displayed for each group. (c) Progressive supranuclear palsy-related pattern (PSPRP; left) identified by spatial covariance analysis of FDG PET scans from 10 PSP patients and 10 healthy controls. This pattern was characterized by covarying metabolic decreases (blue) in the medial prefrontal cortex, the frontal eye fields, the ventrolateral prefrontal cortex, the caudate nuclei, the medial thalamus, and the upper brainstem (Eckert et al., 2008). Neuropathological findings (right) from a patient classified as PSP with a likelihood of 99% by the automated differential diagnosis algorithm based on an FDG PET scan performed 3.9 years before death (Tang et al., 2010b). Postmortem examination confirmed this diagnosis, with characteristic histopathological changes in the pons (top) and frontal cortex (bottom). Argyrophilic globosum neuronal tangles were noted in the basis pontis (Bielschowsky stain 400×). A neuronal tangle with cytoplasmic inclusions and neuropil threads is displayed from the fifth cortical layer of the prefrontal region (AT8 stain, 630×). Tufted astrocytes (not shown) were present in this cortical region, the amygdala, globus pallidus, and claustrum. (d) PSPRP expression was significantly elevated (p < 0.001) in the training group of 10 PSP patients (open diamonds) relative to the 10 healthy controls (open circles) that were used for pattern derivation. Similarly, pattern expression was elevated (p < 0.001) in the two testing groups of PSP patients (closed diamonds) relative to a testing group of healthy controls (closed circles). Mean and standard deviation are also displayed for each group. a and c: Reprinted from Lancet Neurol, Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis, 149–158, Copyright 2010, with permission from Elsevier; b and d: Reprinted from Mov Disord, Abnormal metabolic networks in atypical parkinsonism, 727–733, Copyright 2008, with permission from John Wiley & Sons, Inc. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this book.)

172

(MSA or PSP) who would be ideal candidates for clinical trials, particularly those aimed at evaluat­ ing disease-modifying agents in the earliest phase of the disease. This technique could also be bene­ ficial for diagnostic confirmation of PD prior to invasive treatments, such as DBS and gene trans­ fer therapy, which are likely to be less effective or possibly deleterious in atypical patients (Shih and Tarsy, 2007). In addition, the incorporation of a fully automated algorithm enables our approach to be unbiased by clinical impression and does not require special technical expertise in carrying out the test. Future directions There are three main conclusions we want to draw from the results discussed here. First, network analysis on human FDG PET data has revealed three specific disease-related spatial covariance patterns that are associated with distinct clinical manifestations in PD and faithfully trace the pro­ gression and treatment response of these different aspects of the disease (akinetic-rigid motor mani­ festations, tremor, and cognitive dysfunction). Second, comparison of networks for IPD, MSA, and PSP within a fully automated algorithm enables surprisingly accurate differential diagnosis of individual patients with parkinsonism even in early stages of disease. Third, these studies shed light on the mechanisms of compensatory changes in preclinical period and potentially those that mediate the side effects of chronic treatment such as LID. In this vein, it will be interesting to use this approach to study individuals at risk of developing PD, such as those with REM sleep behavior disorder (Albin et al., 2000; Eisensehr et al., 2000). In particular, it is relevant to know whether they display abnormalities in the expression of established metabolic networks, or whether they exhibit novel patterns that are specific to this preclinical PD population. It would also be interesting to investigate the pathophysiological basis of PDCP expression in

non-demented PD patients. For example, do the metabolic reductions observed regionally in fron­ tal and parietal association cortices reflect local histopathological change within these brain areas, or the functional effects of the loss of ascending dopaminergic and/or cholinergic pro­ jections? The involvement in the PDCP of the neo-cortical regions with the earliest histopatholo­ gical changes (Braak et al., 2003, 2005) is consis­ tent with the first possibility, although the latter cannot be excluded in individual patients with sig­ nificant pharmacological modulation of network activity. Moreover, other histopathological changes can also contribute to the regional abnormalities observed within this network. For instance, some of these regions may be directly or indirectly involved by coincident Alzheimer-type pathology (Jellinger et al., 2002). To address these issues, a multi-tracer PET approach is being con­ ducted to compare the cortical metabolic reduc­ tions seen in non-demented PD patients with the estimates of local protein aggregation in the same subjects. The goal of the study will be to deter­ mine whether abnormal cortical metabolic activity is associated with the accumulation of aggregated protein deposits, and whether those changes impact upon the response of PD patients to phar­ macological interventions targeting the cognitive manifestations of this disorder. Our automated differential diagnosis algorithm can potentially be expanded to include other atypical parkinsonian conditions, e.g., cortico­ basal ganglionic degeneration (CBGD). Although MSA and PSP account for the majority of APSs (Hughes et al., 1992b, 2002), CBGD accounts for approximately 5% of APSs. Because of the rarity of this and other APS conditions, it will take longer to identify and validate disease-related metabolic patterns for these unusual conditions. That said, the availability of antemortem FDG PET scans from patients with autopsy-proven diagnoses has facilitated the characterization of new and highly specific network biomarkers for CBGD and other less frequently encountered par­ kinsonian variant disorders. It will certainly be

173

valuable to “update” the present differential diag­ nosis algorithm with new disease-related patterns as they become available. More relevant perhaps will be to test the performance of this pattern recognition method in a broader multi-center con­ text. The ultimate validity of this and related diag­ nostic methods rests on the study of strictly defined populations under blinded conditions. Most critically, rigorous case ascertainment proce­ dures will be required over long-term clinical fol­ low-up, ideally with pathological confirmation.

Vim

Acknowledgments This work was supported by the National Insti­ tutes of Health [NINDS R01 NS 35069 and P50 NS 38370 to D.E.] and the General Clinical Research Center of The Feinstein Institute for Medical Research, North Shore-LIJ Health Sys­ tem [National Center for Research Resources (NCRR), a component of the National Institutes of Health, M01 RR018535]. The authors wish to thank Dr. Vicky Brandt, Mr. Noam Gerber, and Ms. Toni Fitzpatrick for valuable editorial assistance.

List of Abbreviations AAV APS CBGD CSPTC DBS FDG GAD LID MCI MSA MSARP NPV

PET PD PDCP PDRP PDTP PPV PSP PSPRP REM STN UPDRS

adeno-associated virus atypical parkinsonian syndromes cortico-basal ganglionic degeneration cortico-striato-pallido­ thalamocortical deep brain stimulation [18F]-fluorodeoxyglucose glutamic acid decarboxylase levodopa-induced dyskinesias mild cognitive impairment multiple system atrophy MSA-related pattern negative predictive value

positron emission tomography Parkinson’s disease PD-related cognitive pattern PD-related motor pattern PD-related tremor pattern positive predictive value progressive supranuclear palsy PSP-related pattern rapid eye movement the subthalamic nucleus Unified Parkinson’s Disease Rating Scale ventral-intermediate

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